|
33 | 33 | * [[code](https://github.com/carpedm20/visual-analogy-tensorflow)] |
34 | 34 | * [[paper](http://www-personal.umich.edu/~reedscot/nips2015.pdf)] |
35 | 35 |
|
36 | | -#### Deep Convolutional Generative Adversarial Networks |
| 36 | +#### Deep Convolutional Generative Adversarial Networks |
37 | 37 | * Alec Radford, Luke Metz, Soumith Chintala, arxiv, 2015 |
38 | 38 | * [[code](https://github.com/carpedm20/DCGAN-tensorflow)] |
39 | 39 | * [[paper](http://arxiv.org/pdf/1511.06434v2.pdf)] |
|
58 | 58 | * [[code](https://github.com/DeepLearningProjects/DeepLearningFlappyBird)] |
59 | 59 | * [[report](http://cs229.stanford.edu/proj2015/362_report.pdf)] |
60 | 60 |
|
61 | | -#### Semi-Supervised Learning with Ladder Network |
| 61 | +#### Semi-Supervised Learning with Ladder Network |
62 | 62 | * A Rasmus, H Valpola, M Honkala, M Berglund, and T Raiko, NIPS, 2015 |
63 | 63 | * [[code](https://github.com/rinuboney/ladder)] |
64 | 64 | * [[paper](https://papers.nips.cc/paper/5947-semi-supervised-learning-with-ladder-networks.pdf)] |
65 | 65 | * [[Additional Material](http://rinuboney.github.io/2016/01/19/ladder-network.html)] |
66 | 66 |
|
| 67 | + |
67 | 68 | #### Convolutional Neural Networks for Sentence Classification |
68 | 69 | * Yoon Kim, EMNLP, 2014 |
69 | 70 | * [[code](https://github.com/dennybritz/cnn-text-classification-tf)] |
70 | 71 | * [[paper](http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf)] |
71 | 72 | * [[Additional Material](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/)] |
72 | 73 |
|
| 74 | +### Deep Residual Learning for Image Recognition |
| 75 | + * K He, X Zhang, S Ren, J Sun |
| 76 | + * [[code](https://github.com/ppwwyyxx/tensorpack/tree/master/examples/ResNet)] for cifar10 |
| 77 | + * [[paper1](http://arxiv.org/abs/1512.03385)], [[paper2](http://arxiv.org/abs/1603.05027)] |
| 78 | + |
73 | 79 | ## Contribution |
74 | 80 | Did you find new TensorFlow implementations? Pull requests are always welcome. |
| 81 | + |
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