# TensorFlow Research Models

This folder contains machine learning models implemented by researchers in
[TensorFlow](https://tensorflow.org). The models are maintained by their
respective authors. To propose a model for inclusion, please submit a pull
request.

Currently, the models are compatible with TensorFlow 1.0 or later. If you are
running TensorFlow 0.12 or earlier, please
[upgrade your installation](https://www.tensorflow.org/install).


## Models
- [adversarial_crypto](adversarial_crypto): protecting communications with adversarial neural cryptography.
- [adversarial_text](adversarial_text): semi-supervised sequence learning with adversarial training.
- [attention_ocr](attention_ocr): a model for real-world image text extraction.
- [audioset](audioset): Models and supporting code for use with [AudioSet](http://g.co.audioset).
- [autoencoder](autoencoder): various autoencoders.
- [cognitive_mapping_and_planning](cognitive_mapping_and_planning): implementation of a spatial memory based mapping and planning architecture for visual navigation.
- [compression](compression): compressing and decompressing images using a pre-trained Residual GRU network.
- [differential_privacy](differential_privacy): privacy-preserving student models from multiple teachers.
- [domain_adaptation](domain_adaptation): domain separation networks.
- [im2txt](im2txt): image-to-text neural network for image captioning.
- [inception](inception): deep convolutional networks for computer vision.
- [learning_to_remember_rare_events](learning_to_remember_rare_events):  a large-scale life-long memory module for use in deep learning.
- [lfads](lfads): sequential variational autoencoder for analyzing neuroscience data.
- [lm_1b](lm_1b): language modeling on the one billion word benchmark.
- [namignizer](namignizer): recognize and generate names.
- [neural_gpu](neural_gpu): highly parallel neural computer.
- [neural_programmer](neural_programmer): neural network augmented with logic and mathematic operations.
- [next_frame_prediction](next_frame_prediction): probabilistic future frame synthesis via cross convolutional networks.
- [object_detection](object_detection): localizing and identifying multiple objects in a single image.
- [pcl_rl](pcl_rl): code for several reinforcement learning algorithms, including Path Consistency Learning.
- [ptn](ptn): perspective transformer nets for 3D object reconstruction.
- [qa_kg](qa_kg): module networks for question answering on knowledge graphs.
- [real_nvp](real_nvp): density estimation using real-valued non-volume preserving (real NVP) transformations.
- [rebar](rebar): low-variance, unbiased gradient estimates for discrete latent variable models.
- [resnet](resnet): deep and wide residual networks.
- [skip_thoughts](skip_thoughts): recurrent neural network sentence-to-vector encoder.
- [slim](slim): image classification models in TF-Slim.
- [street](street): identify the name of a street (in France) from an image using a Deep RNN.
- [swivel](swivel): the Swivel algorithm for generating word embeddings.
- [syntaxnet](syntaxnet): neural models of natural language syntax.
- [textsum](textsum): sequence-to-sequence with attention model for text summarization.
- [transformer](transformer): spatial transformer network, which allows the spatial manipulation of data within the network.
- [video_prediction](video_prediction): predicting future video frames with neural advection.
