@@ -24,7 +24,6 @@ The research models are maintained by their respective authors.
| [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 |
| [astronet](astronet) | AstroNet<br/>[1] [Identifying Exoplanets with Deep Learning: A Five Planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90](https://arxiv.org/abs/1712.05044) | A neural network for identifying exoplanets in light curves | cshallue|
| [attention_ocr](attention_ocr) | [Attention-based Extraction of Structured Information from Street View Imagery](https://arxiv.org/abs/1704.03549) | | alexgorban |
| [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|
