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chainer-examples

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This is implementation examples by Chainer.

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# chainer-example
Implementation of various neural networks with chainer.

* [Chainer](https://chainer.org/)
* [Chainer GitHub Repo](https://github.com/chainer/chainer)
* [Chainer documentation](https://docs.chainer.org/en/stable/index.html)

## Regression
- Logistic Regression
- Linear Regression

## CNN
Convolutional Networks
- LeNet: [Gradient-Based Learning Applied to Document Recognition](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf)
- AlexNet: [ImageNet Classification with Deep Convolutional Neural Networks](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
- VGG19: [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/pdf/1409.1556.pdf)
- VGG16: [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/pdf/1409.1556.pdf)
- GoogleNet: [ImageNet Large Scale Visual Recognition Challenge](http://hci.stanford.edu/publications/2015/scenegraphs/imagenet-challenge.pdf)

## ResNet
ResNet and its variants
- ResNet: [Deep Residual Learning for Image Recognition](http://arxiv.org/pdf/1512.03385v1.pdf)
- DenseNet: [Densely Connected Convolutional Networks](https://arxiv.org/pdf/1608.06993.pdf)
- ResNext: [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf)

## AutoEncoder
- VAE: [Auto-Encoding Variational Bayes](https://arxiv.org/abs/1312.6114)

## GAN
- DCGAN: [UNSUPERVISED REPRESENTATION LEARNING WITH DEEP CONVOLUTIONAL GENERATIVE ADVERSARIAL NETWORKS](https://arxiv.org/pdf/1511.06434.pdf)
- BEGAN: [Boundary Equilibrium Generative Adversarial Networks](https://arxiv.org/abs/1703.10717)
- WGAN: [Wasserstein GAN](https://arxiv.org/abs/1701.07875)
- WGAN-GP: [Improved Training of Wasserstein GANs](https://arxiv.org/abs/1704.00028)

## MNIST
- MLP
- CNN
- DCGAN

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