{"id":15029878,"url":"https://github.com/tdeboissiere/deeplearningimplementations","last_synced_at":"2025-05-15T16:02:28.925Z","repository":{"id":70570233,"uuid":"67865666","full_name":"tdeboissiere/DeepLearningImplementations","owner":"tdeboissiere","description":"Implementation of recent Deep Learning 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Implementation of recent Deep Learning papers\n\n# Papers\n\n- [Densely Connected Convolutional Network](http://arxiv.org/abs/1608.06993) implemented in the [DenseNet folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DenseNet)\n\n- [Visualizing and Understanding Convolutional Networks](https://arxiv.org/pdf/1311.2901v1) implemented in the [DeconvNet folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DeconvNet)\n\n- [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)\n\n- [Colorful Image Colorization](https://arxiv.org/abs/1603.08511) implemented in the [Colorful folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/Colorful)\n\n- [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)\n\n- Several Generative Adversarial Networks (GAN) models and techniques in the [GAN folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/GAN)\n\n- [pix2pix](https://arxiv.org/pdf/1611.07004v1.pdf) in the [pix2pix folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/pix2pix)\n\n- [InfoGAN](https://arxiv.org/abs/1606.03657) in the [InfoGAN folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/InfoGAN)\n\n- [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.\n\n- [BEGAN](https://arxiv.org/pdf/1703.10717.pdf) in the [BEGAN folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/BEGAN)\n\n- [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)\n\n- [Sobolev Training for Neural Networks](https://arxiv.org/abs/1706.04859) in the [Sobolev folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/Sobolev)\n\n- [Self-Normalizing Networks](https://arxiv.org/pdf/1706.02515.pdf) in the [SELU folder](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/SELU)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftdeboissiere%2Fdeeplearningimplementations","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftdeboissiere%2Fdeeplearningimplementations","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftdeboissiere%2Fdeeplearningimplementations/lists"}