awesome-very-deep-learning

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awesome-very-deep-learning is a curated list for papers and code about implementing and training very deep neural networks.

Deep Residual Learning

Deep Residual Networks are a family of extremely deep architectures (up to 1000 layers) showing compelling accuracy and nice convergence behaviors. Instead of learning a new representation at each layer, deep residual networks use identity mappings to learn residuals.

Papers

Implementations

  1. Torch by Facebook AI Research (FAIR), with training code in Torch and pre-trained ResNet-18/34/50/101 models for ImageNet: blogcode
  2. Torch, CIFAR-10, with ResNet-20 to ResNet-110, training code, and curves: code
  3. Lasagne, CIFAR-10, with ResNet-32 and ResNet-56 and training code: code
  4. Neon, CIFAR-10, with pre-trained ResNet-32 to ResNet-110 models, training code, and curves: code
  5. Neon, Preactivation layer implementation: code
  6. Torch, MNIST, 100 layers: blogcode
  7. A winning entry in Kaggle‘s right whale recognition challenge: blogcode
  8. Neon, Place2 (mini), 40 layers: blogcode
  9. Tensorflow with tflearn, with CIFAR-10 and MNIST: code
  10. Tensorflow with skflow, with MNIST: code
  11. Stochastic dropout in Keras: code
  12. ResNet in Chainer: code
  13. Stochastic dropout in Chainer: code
  14. Wide Residual Networks in Keras: code
  15. ResNet in TensorFlow 0.9+ with pretrained caffe weights: code

In addition, this code by Ryan Dahl helps to convert the pre-trained models to TensorFlow.

Highway Networks

Highway Networks take inspiration from Long Short Term Memory (LSTM) and allow training of deep, efficient networks (with hundreds of layers) with conventional gradient-based methods

Papers

Implementations

  1. Lasagne: code
  2. Caffe: code
  3. Torch: code
  4. Tensorflow: blogcode

Very Deep Learning Theory

Theories in very deep learning concentrate on the ideas that very deep networks with skip connections are able to efficiently approximate recurrent computations (similar to the recurrent connections in the visual cortex) or are actually exponential ensembles of shallow networks

Papers

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