Add new templates and update README files (#8467)

* Update README.md

Add a new section (Announcements)

* Update README.md

Remove description columns from tables
Remove guidelines sections

* Update CONTRIBUTING.md

Add a link to the wiki for guidelines

* Create README_TEMPLATE.md

Add a README template file for a new model

* Create pull_request_template.md

Add a new template for a pull request

* Update README.md

Add a table of contents
Add a new section for archived models and implementations

* Update README.md

Revise "Contributions"

* Update README.md

Update the link to the contribution guidelines

* Update README.md

Update the link to the contribution guidelines.

* Update README.md

Update the link to the contribution guidelines.

* Update README.md

Update the link to the contribution guidelines.

.github/PULL_REQUEST_TEMPLATE/pull_request_template.md

+# Description
+
+> :memo: Please include a summary of the change. 
+>  
+> * Please also include relevant motivation and context.  
+> * List any dependencies that are required for this change.  
+
+## Type of change
+
+Please delete options that are not relevant.
+
+- [ ] Bug fix (non-breaking change which fixes an issue)
+- [ ] Documentation update
+- [ ] TensorFlow 2 migration
+- [ ] New feature (non-breaking change which adds functionality)
+- [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
+- [ ] A new research paper code implementation
+
+## Tests
+
+> :memo: Please describe the tests that you ran to verify your changes.
+>  
+> * Provide instructions so we can reproduce.  
+> * Please also list any relevant details for your test configuration.  
+
+**Test Configuration**:
+
+## Checklist
+
+- [ ] I have signed the [Contributor License Agreement](../../wiki/Contributor-License-Agreements).
+- [ ] I have read [guidelines for pull request](../../wiki/Submitting-a-pull-request).
+- [ ] My code follows the [coding guidelines](../../wiki/Coding-Guidelines).
+- [ ] I have performed a self [code review](../../wiki/Code-review) of my own code.
+- [ ] I have commented my code, particularly in hard-to-understand areas.
+- [ ] I have made corresponding changes to the documentation.
+- [ ] My changes generate no new warnings.
+- [ ] I have added tests that prove my fix is effective or that my feature works.

.github/README_TEMPLATE.md

+> :memo: A README.md template for releasing a paper code implementation to a GitHub repository.  
+>  
+> * Template version: 1.0.2020.125  
+> * Please modify sections depending on needs.  
+
+# Model name, Paper title, or Project Name
+
+> :memo: Add a badge for the ArXiv identifier of your paper (arXiv:YYMM.NNNNN)
+
+[![Paper](http://img.shields.io/badge/paper-arXiv.YYMM.NNNNN-B3181B.svg)](https://arxiv.org/abs/...)
+
+This repository is the official or unofficial implementation of the following paper.
+
+* Paper title: [Paper Title](https://arxiv.org/abs/YYMM.NNNNN)
+
+## Description
+
+> :memo: Provide description of the model.  
+>  
+> * Provide brief information of the algorithms used.  
+> * Provide links for demos, blog posts, etc.  
+
+## History
+
+> :memo: Provide a changelog.
+
+## Maintainers
+
+> :memo: Provide maintainer information.  
+
+* Last name, First name ([@GitHub username](https://github.com/username))
+* Last name, First name ([@GitHub username](https://github.com/username))
+
+## Table of Contents
+
+> :memo: Provide a table of contents to help readers navigate a lengthy README document.
+
+## Requirements
+
+[![tensorFlow 2.1+](https://img.shields.io/badge/TensorFlow-2.1-brightgreen)](https://github.com/tensorflow/tensorflow/releases/tag/v2.1.0)
+[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)
+
+> :memo: Provide details of the software required.  
+>  
+> * Add a `requirements.txt` file to the root directory for installing the necessary dependencies.  
+>   * Describe how to install requirements using pip.  
+> * Alternatively, create INSTALL.md.  
+
+To install requirements:
+
+```setup
+pip install -r requirements.txt
+```
+
+## Results
+
+> :memo: Provide a table with results. (e.g., accuracy, latency)  
+>  
+> * Provide links to the pre-trained models (checkpoint, SavedModel files).  
+>   * Publish TensorFlow SavedModel files on TensorFlow Hub (tfhub.dev) if possible.  
+> * Add links to [TensorBoard.dev](https://tensorboard.dev/) for visualizing metrics.  
+>  
+> An example table for image classification results  
+>  
+> ### Image Classification  
+>  
+> | Model name | Download | Top 1 Accuracy | Top 5 Accuracy |  
+> |------------|----------|----------------|----------------|  
+> | Model name | [Checkpoint](https://drive.google.com/...), [SavedModel](https://tfhub.dev/...) | xx% | xx% |  
+
+## Dataset
+
+> :memo: Provide information of the dataset used.  
+
+## Training
+
+> :memo: Provide training information.  
+>  
+> * Provide details for preprocessing, hyperparameters, random seeds, and environment.  
+> * Provide a command line example for training.  
+
+Please run this command line for training.
+
+```shell
+python3 ...
+```
+
+## Evaluation
+
+> :memo: Provide an evaluation script with details of how to reproduce results.  
+>  
+> * Describe data preprocessing / postprocessing steps.  
+> * Provide a command line example for evaluation.  
+
+Please run this command line for evaluation.
+
+```shell
+python3 ...
+```
+
+## References
+
+> :memo: Provide links to references.  
+
+## License
+
+> :memo: Place your license text in a file named LICENSE in the root of the repository.  
+>  
+> * Include information about your license.  
+> * Reference: [Adding a license to a repository](https://help.github.com/en/github/building-a-strong-community/adding-a-license-to-a-repository)  
+
+This project is licensed under the terms of the **Apache License 2.0**.
+
+## Citation
+
+> :memo: Make your repository citable.  
+>  
+> * Reference: [Making Your Code Citable](https://guides.github.com/activities/citable-code/)  
+
+If you want to cite this repository in your research paper, please use the following information.

CONTRIBUTING.md

-# Contributing guidelines
+# How to contribute
 
-If you have created a model and would like to publish it here, please send us a
-pull request. For those just getting started with pull requests, GitHub has a
-[howto](https://help.github.com/articles/using-pull-requests/).
+![Contributors](https://img.shields.io/github/contributors/tensorflow/models)
 
-The code for any model in this repository is licensed under the Apache License
-2.0.
+We encourage you to contribute to the TensorFlow Model Garden.
 
-In order to accept our code, we have to make sure that we can publish your code:
-You have to sign a Contributor License Agreement (CLA).
+Please read our [guidelines](../../wiki/How-to-contribute) for details.
 
-***NOTE***: Only [code owners](./CODEOWNERS) are allowed to merge a pull request. 
+**NOTE**: Only [code owners](./CODEOWNERS) are allowed to merge a pull request.
 Please contact the code owners of each model to merge your pull request.
-
-### Contributor License Agreements
-
-Please fill out either the individual or corporate Contributor License Agreement (CLA).
-
-  * If you are an individual writing original source code and you're sure you own the intellectual property, then you'll need to sign an [individual CLA](http://code.google.com/legal/individual-cla-v1.0.html).
-  * If you work for a company that wants to allow you to contribute your work, then you'll need to sign a [corporate CLA](http://code.google.com/legal/corporate-cla-v1.0.html).
-
-Follow either of the two links above to access the appropriate CLA and instructions for how to sign and return it. Once we receive it, we'll be able to accept your pull requests.
-
-***NOTE***: Only original source code from you and other people that have signed the CLA can be accepted into the repository.
-

README.md

@@ -4,17 +4,19 @@
 
 The TensorFlow Model Garden is a repository with a number of different implementations of state-of-the-art (SOTA) models and modeling solutions for TensorFlow users. We aim to demonstrate the best practices for modeling so that TensorFlow users can take full advantage of TensorFlow for their research and product development.
 
-## Structure
-
-| Folder | Description |
+| Directory | Description |
 |-----------|-------------|
-| [official](official) | • **A collection of example implementations for SOTA models using the latest TensorFlow 2's high-level APIs**<br />• Officially maintained, supported, and kept up to date with the latest TensorFlow 2 APIs<br />• Reasonably optimized for fast performance while still being easy to read |
-| [research](research) | • A collection of research model implementations in TensorFlow 1 or 2 by researchers<br />• Up to the individual researchers to maintain the model implementations and/or provide support on issues and pull requests |
+| [official](official) | • A collection of example implementations for SOTA models using the latest TensorFlow 2's high-level APIs<br />• Officially maintained, supported, and kept up to date with the latest TensorFlow 2 APIs by TensorFlow<br />• Reasonably optimized for fast performance while still being easy to read |
+| [research](research) | • A collection of research model implementations in TensorFlow 1 or 2 by researchers<br />• Maintained and supported by researchers |
 | [community](community) | • A curated list of the GitHub repositories with machine learning models and implementations powered by TensorFlow 2 |
 
-## Contribution guidelines
+## [Announcements](../../wiki/Announcements)
+
+* March 31, 2020: [Introducing the Model Garden for TensorFlow 2](https://blog.tensorflow.org/2020/03/introducing-model-garden-for-tensorflow-2.html) ([Tweet](https://twitter.com/TensorFlow/status/1245029834633297921))
+
+## Contributions
 
-If you want to contribute to models, please review the [contribution guidelines](CONTRIBUTING.md).
+If you want to contribute, please review the [contribution guidelines](../../wiki/How-to-contribute).
 
 ## License
 

community/README.md

@@ -4,7 +4,7 @@
 
 This repository provides a curated list of the GitHub repositories with machine learning models and implementations powered by TensorFlow 2.
 
-**Disclosure**: Contributing companies or individuals are responsible for maintaining their repositories.
+**Note**: Contributing companies or individuals are responsible for maintaining their repositories.
 
 ## Models / Implementations
 
@@ -16,6 +16,6 @@ This repository provides a curated list of the GitHub repositories with machine
 | [Mask R-CNN](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Segmentation/MaskRCNN) | [Mask R-CNN](https://arxiv.org/abs/1703.06870) | • Automatic Mixed Precision<br/>• Multi-GPU training support with Horovod<br/>• TensorRT | [NVIDIA](https://github.com/NVIDIA) |
 | [U-Net Medical Image Segmentation](https://github.com/NVIDIA/DeepLearningExamples/tree/master/TensorFlow2/Segmentation/UNet_Medical) | [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597) | • Automatic Mixed Precision<br/>• Multi-GPU training support with Horovod<br/>• TensorRT | [NVIDIA](https://github.com/NVIDIA) |
 
-
 ## Contributions
-If you have a suggestion for the community model repository, please submit a pull request.
+
+If you want to contribute, please review the [contribution guidelines](../../../wiki/How-to-contribute).

official/README.md

@@ -6,41 +6,68 @@ The TensorFlow official models are a collection of models
 that use TensorFlow’s high-level APIs.
 They are intended to be well-maintained, tested, and kept up to date
 with the latest TensorFlow API.
+
 They should also be reasonably optimized for fast performance while still
 being easy to read.
 These models are used as end-to-end tests, ensuring that the models run
 with the same or improved speed and performance with each new TensorFlow build.
 
-## Model Implementations
+## More models to come!
 
-### Natural Language Processing
+The team is actively developing new models.
+In the near future, we will add:
+
+* State-of-the-art language understanding models:
+  More members in Transformer family
+* Start-of-the-art image classification models:
+  EfficientNet, MnasNet, and variants
+* A set of excellent objection detection models.
+
+## Table of Contents
 
-| Model | Description | Reference |
-| ----- | ----------- | --------- |
-| [ALBERT](nlp/albert) | A Lite BERT for Self-supervised Learning of Language Representations | [arXiv:1909.11942](https://arxiv.org/abs/1909.11942) |
-| [BERT](nlp/bert) | A powerful pre-trained language representation model: BERT (Bidirectional Encoder Representations from Transformers) | [arXiv:1810.04805](https://arxiv.org/abs/1810.04805) |
-| [NHNet](nlp/nhnet) | A transformer-based multi-sequence to sequence model: Generating Representative Headlines for News Stories | [arXiv:2001.09386](https://arxiv.org/abs/2001.09386) |
-| [Transformer](nlp/transformer) | A transformer model to translate the WMT English to German dataset | [arXiv:1706.03762](https://arxiv.org/abs/1706.03762) |
-| [XLNet](nlp/xlnet) | XLNet: Generalized Autoregressive Pretraining for Language Understanding | [arXiv:1906.08237](https://arxiv.org/abs/1906.08237) |
+- [Models and Implementations](#models-and-implementations)
+  * [Computer Vision](#computer-vision)
+    + [Image Classification](#image-classification)
+    + [Object Detection and Segmentation](#object-detection-and-segmentation)
+  * [Natural Language Processing](#natural-language-processing)
+  * [Recommendation](#recommendation)
+- [How to get started with the official models](#how-to-get-started-with-the-official-models)
+
+## Models and Implementations
 
 ### Computer Vision
 
-| Model | Description | Reference |
-| ----- | ----------- | --------- |
-| [MNIST](vision/image_classification) | A basic model to classify digits from the MNIST dataset | [Link](http://yann.lecun.com/exdb/mnist/) |
-| [ResNet](vision/image_classification) | A deep residual network for image recognition | [arXiv:1512.03385](https://arxiv.org/abs/1512.03385) |
-| [RetinaNet](vision/detection) | A fast and powerful object detector | [arXiv:1708.02002](https://arxiv.org/abs/1708.02002) |
-| [Mask R-CNN](vision/detection) | An object detection and instance segmentation model | [arXiv:1703.06870](https://arxiv.org/abs/1703.06870) |
+#### Image Classification
+
+| Model | Reference (Paper) |
+|-------|-------------------|
+| [MNIST](vision/image_classification) | A basic model to classify digits from the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) |
+| [ResNet](vision/image_classification) | [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) |
 
-### Other models
+#### Object Detection and Segmentation
 
-| Model | Description | Reference |
-| ----- | ----------- | --------- |
-| [NCF](recommendation) | Neural Collaborative Filtering model for recommendation tasks | [arXiv:1708.05031](https://arxiv.org/abs/1708.05031) |
+| Model | Reference (Paper) |
+|-------|-------------------|
+| [RetinaNet](vision/detection) | [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002) |
+| [Mask R-CNN](vision/detection) | [Mask R-CNN](https://arxiv.org/abs/1703.06870) |
+
+### Natural Language Processing
 
----
+| Model | Reference (Paper) |
+|-------|-------------------|
+| [ALBERT (A Lite BERT)](nlp/albert) | [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942) |
+| [BERT (Bidirectional Encoder Representations from Transformers)](nlp/bert) | [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) |
+| [NHNet (News Headline generation model)](nlp/nhnet) | [Generating Representative Headlines for News Stories](https://arxiv.org/abs/2001.09386) |
+| [Transformer](nlp/transformer) | [Attention Is All You Need](https://arxiv.org/abs/1706.03762) |
+| [XLNet](nlp/xlnet) | [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) |
 
-## How to get started with the Model Garden official models
+### Recommendation
+
+| Model | Reference (Paper) |
+|-------|-------------------|
+| [NCF](recommendation) | [Neural Collaborative Filtering](https://arxiv.org/abs/1708.05031) |
+
+## How to get started with the official models
 
 * The models in the master branch are developed using TensorFlow 2,
 and they target the TensorFlow [nightly binaries](https://github.com/tensorflow/tensorflow#installation)
@@ -108,44 +135,6 @@ os.environ['PYTHONPATH'] += ":/path/to/models"
 pip3 install --user -r official/requirements.txt
 ```
 
----
-
-## More models to come!
-
-The team is actively developing new models.
-In the near future, we will add:
-
-- State-of-the-art language understanding models:
-  More members in Transformer family
-- Start-of-the-art image classification models:
-  EfficientNet, MnasNet and variants.
-- A set of excellent objection detection models.
-
-If you would like to make any fixes or improvements to the models, please
-[submit a pull request](https://github.com/tensorflow/models/compare).
-
----
-
 ## Contributions
 
-Every model should follow our guidelines to uphold our objectives of readable,
-usable, and maintainable code.
-
-### General Guidelines
-
-- Code should be well documented and tested.
-- Runnable from a blank environment with ease.
-- Trainable on: single GPU/CPU (baseline), multiple GPUs & TPUs
-- Compatible with Python 3 (using [six](https://pythonhosted.org/six/)
-when being compatible with Python 2 is necessary)
-- Conform to
-  [Google Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)
-
-### Implementation Guidelines
-
-These guidelines are to ensure consistent model implementations for
-better readability and maintainability.
-
-- Use [common utility functions](utils)
-- Export SavedModel at the end of the training.
-- Consistent flags and flag-parsing library ([read more here](utils/flags/guidelines.md))
+If you want to contribute, please review the [contribution guidelines](../../../wiki/How-to-contribute).

research/README.md

@@ -2,84 +2,123 @@
 
 # TensorFlow Research Models
 
-This folder contains machine learning models implemented by researchers in [TensorFlow](https://tensorflow.org). 
+This directory contains code implementations and pre-trained models of published research papers.
 
-The research models are maintained by their respective authors. 
+The research models are maintained by their respective authors.
 
-**Note: Some research models are stale and have not updated to the latest TensorFlow 2 yet.**
+## Table of Contents
+- [Modeling Libraries and Models](#modeling-libraries-and-models)
+- [Models and Implementations](#models-and-implementations)
+  * [Computer Vision](#computer-vision)
+  * [Natural Language Processing](#natural-language-processing)
+  * [Audio and Speech](#audio-and-speech)
+  * [Reinforcement Learning](#reinforcement-learning)
+  * [Others](#others)
+- [Archived Models and Implementations](#warning-archived-models-and-implementations) (:no_entry_sign: No longer maintained)
 
----
+## Modeling Libraries and Models
+
+| Directory | Name | Description | Maintainer(s) |
+|-----------|------|-------------|---------------|
+| [object_detection](object_detection) | TensorFlow Object Detection API | A framework that makes it easy to construct, train and deploy object detection models<br /><br />A collection of object detection models pre-trained on the COCO dataset, the Kitti dataset, the Open Images dataset, the AVA v2.1 dataset, and the iNaturalist Species Detection Dataset| @jch1, @tombstone, @pkulzc |
+| [slim](slim) | TensorFlow-Slim Image Classification Model Library | A lightweight high-level API of TensorFlow for defining, training and evaluating image classification models <br />• Inception V1/V2/V3/V4<br />• Inception-ResNet-v2<br />• ResNet V1/V2<br />• VGG 16/19<br />• MobileNet V1/V2/V3<br />• NASNet-A_Mobile/Large<br />• PNASNet-5_Large/Mobile | @sguada, @marksandler2 |
+
+## Models and Implementations
+
+### Computer Vision
+
+| Directory | Referenece (Paper) | Maintainer(s) |
+|-----------|--------------------|---------------|
+| [attention_ocr](attention_ocr) | [Attention-based Extraction of Structured Information from Street View Imagery](https://arxiv.org/abs/1704.03549) | xavigibert |
+| [autoaugment](autoaugment) | [1] [AutoAugment](https://arxiv.org/abs/1805.09501)<br />[2] [Wide Residual Networks](https://arxiv.org/abs/1605.07146)<br />[3] [Shake-Shake regularization](https://arxiv.org/abs/1705.07485)<br />[4] [ShakeDrop Regularization for Deep Residual Learning](https://arxiv.org/abs/1802.02375) | barretzoph |
+| [deeplab](deeplab) | [1] [DeepLabv1](https://arxiv.org/abs/1412.7062)<br />[2] [DeepLabv2](https://arxiv.org/abs/1606.00915)<br />[3] [DeepLabv3](https://arxiv.org/abs/1802.02611)<br />[4] [DeepLabv3+](https://arxiv.org/abs/1706.05587) | aquariusjay, yknzhu |
+| [delf](delf)  | [1] DELF (DEep Local Features): [Large-Scale Image Retrieval with Attentive Deep Local Features](https://arxiv.org/abs/1612.06321)<br />[2] [Detect-to-Retrieve](https://arxiv.org/abs/1812.01584) | andrefaraujo |
+| [lstm_object_detection](lstm_object_detection) | [Mobile Video Object Detection with Temporally-Aware Feature Maps](https://arxiv.org/abs/1711.06368) | yinxiaoli, yongzhe2160, lzyuan |
+| [marco](marco) | [Classification of crystallization outcomes using deep convolutional neural networks](https://arxiv.org/abs/1803.10342) | vincentvanhoucke |
+| [vid2depth](vid2depth) | [Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints](https://arxiv.org/abs/1802.05522) | rezama |
+
+### Natural Language Processing
+
+| Directory | Referenece (Paper) | Maintainer(s) |
+|-----------|--------------------|---------------|
+| [adversarial_text](adversarial_text) | [1] [Adversarial Training Methods for Semi-Supervised Text](https://arxiv.org/abs/1605.07725) Classification<br />[2] [Semi-supervised Sequence Learning](https://arxiv.org/abs/1511.01432) | rsepassi, a-dai |
+| [cvt_text](cvt_text) | [Semi-supervised sequence learning with cross-view training](https://arxiv.org/abs/1809.08370) | clarkkev, lmthang |
+
+### Audio and Speech
 
-## Frameworks / APIs with Models
-| Folder | Framework | Description | Maintainer(s) |
-|--------|-----------|-------------|---------------|
-| [object_detection](object_detection) | TensorFlow Object Detection API | A framework that makes it easy to construct, train and deploy object detection models<br/> | jch1, tombstone, derekjchow, jesu9, dreamdragon, pkulzc |
-| [slim](slim) | TensorFlow-Slim Image Classification Model Library | A lightweight high-level API of TensorFlow for defining, training and evaluating image classification models <br/>• Inception V1/V2/V3/V4<br/>• Inception-ResNet-v2<br/>• ResNet V1/V2<br/>• VGG 16/19<br/>• MobileNet V1/V2/V3<br/>• NASNet-A_Mobile/Large<br/>• PNASNet-5_Large/Mobile | sguada, nathansilberman |
+| Directory | Referenece (Paper) | Maintainer(s) |
+|-----------|--------------------|---------------|
+| [audioset](audioset) | [1] [AudioSet: A Large Scale Dataset of Audio Events](https://research.google/pubs/pub45857/)<br />[2] [CNN Architectures for Large-Scale Audio Classification](https://research.google/pubs/pub45611/) | plakal, dpwe |
+
+### Reinforcement Learning
+
+| Directory | Referenece (Paper) | Maintainer(s) |
+|-----------|--------------------|---------------|
+| [efficient-hrl](efficient-hrl) | [1] [Data-Efficient Hierarchical Reinforcement Learning](https://arxiv.org/abs/1805.08296)<br />[2] [Near-Optimal Representation Learning for Hierarchical Reinforcement Learning](https://arxiv.org/abs/1810.01257) | ofirnachum |
+| [pcl_rl](pcl_rl) | [1] [Improving Policy Gradient by Exploring Under-appreciated Rewards](https://arxiv.org/abs/1611.09321)<br />[2] [Bridging the Gap Between Value and Policy Based Reinforcement Learning](https://arxiv.org/abs/1702.08892)<br />[3] [Trust-PCL: An Off-Policy Trust Region Method for Continuous Control](https://arxiv.org/abs/1707.01891) | ofirnachum |
+
+### Others
+
+| Directory | Referenece (Paper) | Maintainer(s) |
+|-----------|--------------------|---------------|
+| [lfads](lfads) | [LFADS - Latent Factor Analysis via Dynamical Systems](https://doi.org/10.1101/152884) | jazcollins, sussillo |
+| [rebar](rebar) | [REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models](https://arxiv.org/abs/1703.07370) | gjtucker |
 
 ---
 
-## Models / Implementations
-
-| Folder | Paper(s) | Description | Maintainer(s) |
-|--------|----------|-------------|---------------|
-| [adv_imagenet<br />_models](adv_imagenet_models)   | [1] [Adversarial Machine Learning at Scale](https://arxiv.org/abs/1611.01236)<br/>[2] [Ensemble Adversarial Training: Attacks and Defenses](https://arxiv.org/abs/1705.07204) | Adversarially trained ImageNet models  | alexeykurakin  |
-| [adversarial_crypto](adversarial_crypto) | [Learning to Protect Communications with Adversarial Neural Cryptography](https://arxiv.org/abs/1610.06918) | Code to train encoder/decoder/adversary network triplets and evaluate their effectiveness on randomly generated input and key pairs | dave-andersen |
-| [adversarial<br />_logit_pairing](adversarial_logit_pairing)   | [Adversarial Logit Pairing](https://arxiv.org/abs/1803.06373) | Implementation of Adversarial logit pairing paper as well as few models pre-trained on ImageNet and Tiny ImageNet   | alexeykurakin |
-| [adversarial_text](adversarial_text) | [1] [Adversarial Training Methods for Semi-Supervised Text](https://arxiv.org/abs/1605.07725) Classification<br/>[2] [Semi-supervised Sequence Learning](https://arxiv.org/abs/1511.01432) | Adversarial Training Methods for Semi-Supervised Text Classification| rsepassi, a-dai |
-| [attention_ocr](attention_ocr)   | [Attention-based Extraction of Structured Information from Street View Imagery](https://arxiv.org/abs/1704.03549) | | xavigibert |
-| [audioset](audioset) | Models for AudioSet: A Large Scale Dataset of Audio Events | | plakal, dpwe |
-| [autoaugment](autoaugment) | [1] [AutoAugment](https://arxiv.org/abs/1805.09501)<br/>[2] [Wide Residual Networks](https://arxiv.org/abs/1605.07146)<br/>[3] [Shake-Shake regularization](https://arxiv.org/abs/1705.07485)<br/>[4] [ShakeDrop Regularization for Deep Residual Learning](https://arxiv.org/abs/1802.02375) | Train Wide-ResNet, Shake-Shake and ShakeDrop models on CIFAR-10 and CIFAR-100 dataset with AutoAugment | barretzoph |
-| [autoencoder](autoencoder) | Various autoencoders | | snurkabill |
-| [brain_coder](brain_coder) | [Neural Program Synthesis with Priority Queue Training](https://arxiv.org/abs/1801.03526) | Program synthesis with reinforcement learning  | danabo |
-| [cognitive_mapping<br />_and_planning](cognitive_mapping_and_planning) | [Cognitive Mapping and Planning for Visual Navigation](https://arxiv.org/abs/1702.03920) | Implementation of a spatial memory based mapping and planning architecture for visual navigation | s-gupta |
-| [compression](compression) | [Full Resolution Image Compression with Recurrent Neural Networks](https://arxiv.org/abs/1608.05148) | | nmjohn |
-| [cvt_text](cvt_text) | [Semi-supervised sequence learning with cross-view training](https://arxiv.org/abs/1809.08370) | | clarkkev, lmthang |
-| [deep_contextual<br />_bandits](deep_contextual_bandits) | [Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling](https://arxiv.org/abs/1802.09127) | | rikel |
-| [deep_speech](deep_speech) | [Deep Speech 2](https://arxiv.org/abs/1512.02595) | End-to-End Speech Recognition in English and Mandarin | |
-| [deeplab](deeplab)  | [1] [DeepLabv1](https://arxiv.org/abs/1412.7062)<br/>[2] [DeepLabv2](https://arxiv.org/abs/1606.00915)<br/>[3] [DeepLabv3](https://arxiv.org/abs/1802.02611)<br/>[4] [DeepLabv3+](https://arxiv.org/abs/1706.05587) | DeepLab models for semantic image segmentation | aquariusjay, yknzhu, gpapan |
-| [delf](delf)  | [1] [Large-Scale Image Retrieval with Attentive Deep Local Features](https://arxiv.org/abs/1612.06321) <br/>[2] [Detect-to-Retrieve](https://arxiv.org/abs/1812.01584) | DELF: DEep Local Features | andrefaraujo |
-| [domain_adaptation](domain_adaptation) | [1] [Domain Separation Networks](https://arxiv.org/abs/1608.06019) <br/>[2] [Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks](https://arxiv.org/abs/1612.05424) | Code used for two domain adaptation papers| bousmalis, dmrd |
-| [efficient-hrl](efficient-hrl) | [1] [Data-Efficient Hierarchical Reinforcement Learning](https://arxiv.org/abs/1805.08296)<br/>[2] [Near-Optimal Representation Learning for Hierarchical Reinforcement Learning](https://arxiv.org/abs/1810.01257) | Code for performing hierarchical reinforcement learning | ofirnachum |
-| [feelvos](feelvos)| [FEELVOS](https://arxiv.org/abs/1902.09513) | Fast End-to-End Embedding Learning for Video Object Segmentation | |
-| [fivo](fivo)| [Filtering variational objectives for training generative sequence models](https://arxiv.org/abs/1705.09279) | | dieterichlawson |
-| [global_objectives](global_objectives) | [Scalable Learning of Non-Decomposable Objectives](https://arxiv.org/abs/1608.04802) | TensorFlow loss functions that optimize directly for a variety of objectives including AUC, recall at precision, and more | mackeya-google |
-| [im2txt](im2txt) | [Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge](https://arxiv.org/abs/1609.06647) | Image-to-text neural network for image captioning| cshallue |
-| [inception](inception) | [Rethinking the Inception Architecture for Computer Vision](https://arxiv.org/abs/1512.00567) | Deep convolutional networks for computer vision | shlens, vincentvanhoucke |
-| [keypointnet](keypointnet) | [KeypointNet](https://arxiv.org/abs/1807.03146) | Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning | mnorouzi |
-| [learned_optimizer](learned_optimizer) | [Learned Optimizers that Scale and Generalize](https://arxiv.org/abs/1703.04813) | | olganw, nirum |
-| [learning_to<br />_remember<br />_rare_events](learning_to_remember_rare_events) | [Learning to Remember Rare Events](https://arxiv.org/abs/1703.03129) | A large-scale life-long memory module for use in deep learning | lukaszkaiser, ofirnachum |
-| [learning<br />_unsupervised<br />_learning](learning_unsupervised_learning) | [Meta-Learning Update Rules for Unsupervised Representation Learning](https://arxiv.org/abs/1804.00222) | A meta-learned unsupervised learning update rule| lukemetz, nirum |
-| [lexnet_nc](lexnet_nc) | LexNET | Noun Compound Relation Classification | vered1986, waterson |
-| [lfads](lfads) | [LFADS - Latent Factor Analysis via Dynamical Systems](https://doi.org/10.1101/152884) | Sequential variational autoencoder for analyzing neuroscience data| jazcollins, sussillo |
-| [lm_1b](lm_1b) | [Exploring the Limits of Language Modeling](https://arxiv.org/abs/1602.02410) | Language modeling on the one billion word benchmark | oriolvinyals, panyx0718 |
-| [lm_commonsense](lm_commonsense) | [A Simple Method for Commonsense Reasoning](https://arxiv.org/abs/1806.02847) | Commonsense reasoning using language models | thtrieu |
-| [lstm_object_detection](lstm_object_detection) | [Mobile Video Object Detection with Temporally-Aware Feature Maps](https://arxiv.org/abs/1711.06368) | | dreamdragon, masonliuw, yinxiaoli, yongzhe2160 |
-| [marco](marco) | [Classification of crystallization outcomes using deep convolutional neural networks](https://arxiv.org/abs/1803.10342) | | vincentvanhoucke |
-| [maskgan](maskgan)| [MaskGAN: Better Text Generation via Filling in the______](https://arxiv.org/abs/1801.07736) | Text generation with GANs | liamb315, a-dai |
-| [namignizer](namignizer)| Namignizer | Recognize and generate names | knathanieltucker |
-| [neural_gpu](neural_gpu)| [Neural GPUs Learn Algorithms](https://arxiv.org/abs/1511.08228) | Highly parallel neural computer | lukaszkaiser |
-| [neural_programmer](neural_programmer) | [Learning a Natural Language Interface with Neural Programmer](https://arxiv.org/abs/1611.08945) | Neural network augmented with logic and mathematic operations| arvind2505 |
-| [next_frame<br />_prediction](next_frame_prediction) | [Visual Dynamics](https://arxiv.org/abs/1607.02586) | Probabilistic Future Frame Synthesis via Cross Convolutional Networks| panyx0718 |
-| [pcl_rl](pcl_rl) | [1] [Improving Policy Gradient by Exploring Under-appreciated Rewards](https://arxiv.org/abs/1611.09321)<br/>[2] [Bridging the Gap Between Value and Policy Based Reinforcement Learning](https://arxiv.org/abs/1702.08892)<br/>[3] [Trust-PCL: An Off-Policy Trust Region Method for Continuous Control](https://arxiv.org/abs/1707.01891) | Code for several reinforcement learning algorithms | ofirnachum |
-| [ptn](ptn) | [Perspective Transformer Nets](https://arxiv.org/abs/1612.00814) | Learning Single-View 3D Object Reconstruction without 3D Supervision | xcyan, arkanath, hellojas, honglaklee |
-| [qa_kg](qa_kg) | [Learning to Reason](https://arxiv.org/abs/1704.05526) | End-to-End Module Networks for Visual Question Answering | yuyuz |
-| [real_nvp](real_nvp) | [Density estimation using Real NVP](https://arxiv.org/abs/1605.08803) | | laurent-dinh |
-| [rebar](rebar) | [REBAR](https://arxiv.org/abs/1703.07370) | Low-variance, unbiased gradient estimates for discrete latent variable models | gjtucker |
-| [sentiment<br />_analysis](sentiment_analysis)| [Effective Use of Word Order for Text Categorization with Convolutional Neural Networks](https://arxiv.org/abs/1412.1058) | A simple model to classify a document's sentiment | sculd |
-| [seq2species](seq2species) | [Seq2Species: A deep learning approach to pattern recognition for short DNA sequences](https://doi.org/10.1101/353474) | Neural Network Models for Species Classification| apbusia, depristo |
-| [skip_thoughts](skip_thoughts) | [Skip-Thought Vectors](https://arxiv.org/abs/1506.06726) | Recurrent neural network sentence-to-vector encoder | cshallue|
-| [steve](steve) | [Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion](https://arxiv.org/abs/1807.01675) | A hybrid model-based/model-free reinforcement learning algorithm for sample-efficient continuous control | buckman-google |
-| [street](street) | [End-to-End Interpretation of the French Street Name Signs Dataset](https://arxiv.org/abs/1702.03970) | Identify the name of a street (in France) from an image using a Deep RNN| theraysmith |
-| [struct2depth](struct2depth)| [Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos](https://arxiv.org/abs/1811.06152) | Unsupervised learning of depth and ego-motion| aneliaangelova |
-| [swivel](swivel) | [Swivel: Improving Embeddings by Noticing What's Missing](https://arxiv.org/abs/1602.02215) | The Swivel algorithm for generating word embeddings | waterson |
-| [tcn](tcn) | [Time-Contrastive Networks: Self-Supervised Learning from Video](https://arxiv.org/abs/1704.06888) | Self-supervised representation learning from multi-view video | coreylynch, sermanet |
-| [textsum](textsum)| Sequence-to-sequence with attention model for text summarization | | panyx0718, peterjliu |
-| [transformer](transformer) | [Spatial Transformer Network](https://arxiv.org/abs/1506.02025) | Spatial transformer network that allows the spatial manipulation of data within the network| daviddao|
-| [vid2depth](vid2depth) | [Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints](https://arxiv.org/abs/1802.05522) | Learning depth and ego-motion unsupervised from raw monocular video | rezama |
-| [video<br />_prediction](video_prediction) | [Unsupervised Learning for Physical Interaction through Video Prediction](https://arxiv.org/abs/1605.07157) | Predicting future video frames with neural advection| cbfinn |
+## :warning: Archived Models and Implementations
+
+The following research models are no longer maintained.
+
+**Note**: We will remove archived models from the master branch in June, 2020. 
+After removal, you will still be able to access archived models in the archive branch.
+
+| Directory | Referenece (Paper) | Maintainer(s) |
+|-----------|--------------------|---------------|
+| [adv_imagenet_models](adv_imagenet_models) | [1] [Adversarial Machine Learning at Scale](https://arxiv.org/abs/1611.01236)<br />[2] [Ensemble Adversarial Training: Attacks and Defenses](https://arxiv.org/abs/1705.07204) | alexeykurakin |
+| [adversarial_crypto](adversarial_crypto) | [Learning to Protect Communications with Adversarial Neural Cryptography](https://arxiv.org/abs/1610.06918) | dave-andersen |
+| [adversarial_logit_pairing](adversarial_logit_pairing) | [Adversarial Logit Pairing](https://arxiv.org/abs/1803.06373) | alexeykurakin |
+| [autoencoder](autoencoder) | Various autoencoders | snurkabill |
+| [brain_coder](brain_coder) | [Neural Program Synthesis with Priority Queue Training](https://arxiv.org/abs/1801.03526) | danabo, mnorouzi |
+| [cognitive_mapping_and_planning](cognitive_mapping_and_planning) | [Cognitive Mapping and Planning for Visual Navigation](https://arxiv.org/abs/1702.03920) | s-gupta |
+| [compression](compression) | [Full Resolution Image Compression with Recurrent Neural Networks](https://arxiv.org/abs/1608.05148) | nmjohn |
+| [deep_contextual_bandits](deep_contextual_bandits) | [Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling](https://arxiv.org/abs/1802.09127) | rikel |
+| [deep_speech](deep_speech) | [Deep Speech 2](https://arxiv.org/abs/1512.02595) | yhliang2018 |
+| [domain_adaptation](domain_adaptation) | [1] [Domain Separation Networks](https://arxiv.org/abs/1608.06019) <br />[2] [Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks](https://arxiv.org/abs/1612.05424) | bousmalis, dmrd |
+| [feelvos](feelvos)| [FEELVOS](https://arxiv.org/abs/1902.09513) | pvoigtlaender, yuningchai, aquariusjay |
+| [fivo](fivo)| [Filtering variational objectives for training generative sequence models](https://arxiv.org/abs/1705.09279) | dieterichlawson |
+| [global_objectives](global_objectives) | [Scalable Learning of Non-Decomposable Objectives](https://arxiv.org/abs/1608.04802) | mackeya-google |
+| [im2txt](im2txt) | [Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge](https://arxiv.org/abs/1609.06647) | cshallue |
+| [inception](inception) | [Rethinking the Inception Architecture for Computer Vision](https://arxiv.org/abs/1512.00567) | shlens, vincentvanhoucke |
+| [keypointnet](keypointnet) | [KeypointNet](https://arxiv.org/abs/1807.03146) | mnorouzi |
+| [learned_optimizer](learned_optimizer) | [Learned Optimizers that Scale and Generalize](https://arxiv.org/abs/1703.04813) | olganw, nirum |
+| [learning_to_remember_rare_events](learning_to_remember_rare_events) | [Learning to Remember Rare Events](https://arxiv.org/abs/1703.03129) | lukaszkaiser, ofirnachum |
+| [learning_unsupervised_learning](learning_unsupervised_learning) | [Meta-Learning Update Rules for Unsupervised Representation Learning](https://arxiv.org/abs/1804.00222) | lukemetz, nirum |
+| [lexnet_nc](lexnet_nc) | [Olive Oil is Made of Olives, Baby Oil is Made for Babies: Interpreting Noun Compounds using Paraphrases in a Neural Model](https://arxiv.org/abs/1803.08073) | vered1986, waterson |
+| [lm_1b](lm_1b) | [Exploring the Limits of Language Modeling](https://arxiv.org/abs/1602.02410) | oriolvinyals, panyx0718 |
+| [lm_commonsense](lm_commonsense) | [A Simple Method for Commonsense Reasoning](https://arxiv.org/abs/1806.02847) | thtrieu |
+| [maskgan](maskgan)| [MaskGAN: Better Text Generation via Filling in the______](https://arxiv.org/abs/1801.07736) | liamb315, a-dai |
+| [namignizer](namignizer)| Namignizer | knathanieltucker |
+| [neural_gpu](neural_gpu)| [Neural GPUs Learn Algorithms](https://arxiv.org/abs/1511.08228) | lukaszkaiser |
+| [neural_programmer](neural_programmer) | [Learning a Natural Language Interface with Neural Programmer](https://arxiv.org/abs/1611.08945) | arvind2505 |
+| [next_frame_prediction](next_frame_prediction) | [Visual Dynamics](https://arxiv.org/abs/1607.02586) | panyx0718 |
+| [ptn](ptn) | [Perspective Transformer Nets](https://arxiv.org/abs/1612.00814) | xcyan, arkanath, hellojas, honglaklee |
+| [qa_kg](qa_kg) | [Learning to Reason](https://arxiv.org/abs/1704.05526) | yuyuz |
+| [real_nvp](real_nvp) | [Density estimation using Real NVP](https://arxiv.org/abs/1605.08803) | laurent-dinh |
+| [sentiment_analysis](sentiment_analysis)| [Effective Use of Word Order for Text Categorization with Convolutional Neural Networks](https://arxiv.org/abs/1412.1058) | sculd |
+| [seq2species](seq2species) | [Seq2Species: A deep learning approach to pattern recognition for short DNA sequences](https://doi.org/10.1101/353474) | apbusia, depristo |
+| [skip_thoughts](skip_thoughts) | [Skip-Thought Vectors](https://arxiv.org/abs/1506.06726) | cshallue |
+| [steve](steve) | [Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion](https://arxiv.org/abs/1807.01675) | buckman-google |
+| [street](street) | [End-to-End Interpretation of the French Street Name Signs Dataset](https://arxiv.org/abs/1702.03970) | theraysmith |
+| [struct2depth](struct2depth)| [Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos](https://arxiv.org/abs/1811.06152) | aneliaangelova |
+| [swivel](swivel) | [Swivel: Improving Embeddings by Noticing What's Missing](https://arxiv.org/abs/1602.02215) | waterson |
+| [tcn](tcn) | [Time-Contrastive Networks: Self-Supervised Learning from Video](https://arxiv.org/abs/1704.06888) | coreylynch, sermanet |
+| [textsum](textsum)| [A Neural Attention Model for Abstractive Sentence Summarization](https://arxiv.org/abs/1509.00685) | panyx0718, peterjliu |
+| [transformer](transformer) | [Spatial Transformer Network](https://arxiv.org/abs/1506.02025) | daviddao|
+| [video_prediction](video_prediction) | [Unsupervised Learning for Physical Interaction through Video Prediction](https://arxiv.org/abs/1605.07157) | cbfinn |
 
 ---
 
 ## Contributions
 
-If you want to contribute a new model, please submit a pull request.
+If you want to contribute, please review the [contribution guidelines](../../../wiki/How-to-contribute).