DEEP LEARNING PAPERS

This repository contains implementations of various Deep learning papers
Models have been trained for a little time and will give better results if trained more. The focus was more on understanding rather than results
An index of papers implemented along with the citations and URLs are as follows

Note: Codes in Pytorch

INDEX

[1] Alex Net

Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012 Paper

[2] VGG Net

Simonyan, Karen, and Andrew Zisserman. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014). Paper

[3] GoogLe Net

Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. Paper

[4] Dropout (Just notes)

Srivastava, Nitish, et al. "Dropout: a simple way to prevent neural networks from overfitting." Journal of Machine Learning Research 15.1 (2014): 1929-1958. Paper

[5] Mobile Net

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (2017), Andrew G. Howard et al. Paper

[6] Inceptionism

[7] DC GAN

Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434. Paper

[8] Spatial Transformer Networks

Jaderberg, M., Simonyan, K., & Zisserman, A. (2015). Spatial transformer networks. In Advances in neural information processing systems (pp. 2017-2025). Paper

[9] Squeeze Net

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 1MB model size (2016), F. Iandola et al. Paper

[10] VAE (Auto-Encoding Variational Bayes)

Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. Paper

[11] SRCNN

Dong, C., Loy, C. C., He, K., & Tang, X. (2014, September). Learning a deep convolutional network for image super-resolution. In European conference on computer vision (pp. 184-199). Springer, Cham. Paper

[12] WGAN

Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein gan. arXiv preprint arXiv:1701.07875. Paper

[13] One cycle (Just notes)

Smith, L. N. (2017, March). Cyclical learning rates for training neural networks. In 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) (pp. 464-472). IEEE. Paper

[14] A disciplined approach to neural network hyper-parameters (Just notes)

Smith, L. N. (2018). A disciplined approach to neural network hyper-parameters: Part 1--learning rate, batch size, momentum, and weight decay. arXiv preprint arXiv:1803.09820. Paper

[15] Class Imbalance Problem (Just notes)

Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural Networks, 106, 249-259. Paper

[16] Perceptual Loss (For super resolution)

[17] Semantic segmentation DeepLab

Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 40(4), 834-848. Paper

[18] Neural Fabrics

Saxena, S., & Verbeek, J. (2016). Convolutional neural fabrics. In Advances in Neural Information Processing Systems (pp. 4053-4061).