Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/tdeboissiere/DeepLearningImplementations
Implementation of recent Deep Learning papers
https://github.com/tdeboissiere/DeepLearningImplementations
deep-learning-papers
Last synced: 4 days ago
JSON representation
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
Awesome Lists containing this project
- 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)