https://github.com/tdeboissiere/DeepLearningImplementations

Implementation of recent Deep Learning papers
https://github.com/tdeboissiere/DeepLearningImplementations

deep-learning-papers

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Implementation of recent Deep Learning papers

• Host: GitHub
• URL: https://github.com/tdeboissiere/DeepLearningImplementations
• Owner: tdeboissiere
• License: mit
• Created: 2016-09-10T10:45:07.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2020-10-23T10:03:46.000Z (about 4 years ago)
• Last Synced: 2024-04-11T07:46:56.435Z (7 months ago)
• Topics: deep-learning-papers
• Language: Python
• Homepage:
• Size: 49.2 MB
• Stars: 1,808
• Watchers: 132
• Forks: 651
• Open Issues: 24
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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• awesome-starred - tdeboissiere/DeepLearningImplementations - Implementation of recent Deep Learning papers (others)

README

        
# Implementation of recent Deep Learning papers

# Papers

- [Densely Connected Convolutional Network](http://arxiv.org/abs/1608.06993) implemented in the [DenseNet folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DenseNet)

- [Visualizing and Understanding Convolutional Networks](https://arxiv.org/pdf/1311.2901v1) implemented in the [DeconvNet folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DeconvNet)

- [Improving Stochastic Gradient Descent With Feedback](https://arxiv.org/pdf/1611.01505v1.pdf) implemented in the [Eve folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/Eve)

- [Colorful Image Colorization](https://arxiv.org/abs/1603.08511) implemented in the [Colorful folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/Colorful)

- [Deep Feature Interpolation for Image Content Changes](https://arxiv.org/pdf/1611.05507v1.pdf) implemented in the [DFI folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DFI)

- Several Generative Adversarial Networks (GAN) models and techniques in the [GAN folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/GAN)

- [pix2pix](https://arxiv.org/pdf/1611.07004v1.pdf) in the [pix2pix folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/pix2pix)

- [InfoGAN](https://arxiv.org/abs/1606.03657) in the [InfoGAN folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/InfoGAN)

- [WassersteinGAN](https://arxiv.org/abs/1701.07875) in the [WassersteinGAN folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/WassersteinGAN) and [WGAN-GP folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/WGAN-GP) for a tensorflow implementation.

- [BEGAN](https://arxiv.org/pdf/1703.10717.pdf) in the [BEGAN folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/BEGAN)

- [Scaling the Scattering Transform: Deep Hybrid Networks](https://arxiv.org/abs/1703.08961) in the [ScatteringTransform folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/ScatteringTransform)

- [Sobolev Training for Neural Networks](https://arxiv.org/abs/1706.04859) in the [Sobolev folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/Sobolev)

- [Self-Normalizing Networks](https://arxiv.org/pdf/1706.02515.pdf) in the [SELU folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/SELU)