# Deep Convolutional Generative Adversarial Nets

## Introduction

* The paper presents Deep Convolutional Generative Adversarial Nets (DCGAN) - a topologically constrained variant of conditional GAN.
* [Link to the paper](https://arxiv.org/abs/1511.06434)

## Benefits

* Stable to train
* Very useful to learn unsupervised image representations.

## Model

* GANs difficult to scale using CNNs.
* Paper proposes following changes to GANs:
    * Replace any pooling layers with strided convolutions (for discriminator) and fractional strided convolutions (for generators).
    * Remove fully connected hidden layers.
    * Use batch normalisation in both generator (all layers except output layer) and discriminator (all layers except input layer).
    * Use LeakyReLU in all layers of the discriminator.
    * Use ReLU activation in all layers of the generator (except output layer which uses Tanh).

## Datasets
    
* Large-Scale Scene Understanding.
* Imagenet-1K.
* Faces dataset.

## Hyperparameters

* Minibatch SGD with minibatch size of 128.
* Weights initialized with 0 centered Normal distribution with standard deviation = 0.02
* Adam    Optimizer
* Slope of leak = 0.2 for LeakyReLU.
* Learning rate = 0.0002, β1 = 0.5

## Observations

* Large-Scale Scene Understanding data
    * Demonstrates that model scales with more data and higher resolution generation.
    * Even though it is unlikely that model would have memorized images (due to low learning rate of minibatch SGD).
* Classifying CIFAR-10 dataset
    * Features
        * Train in Imagenet-1K and test on CIFAR-10.
        * Max pool discriminator's convolutional features (from all layers) to get 4x4 spatial grids.
        * Flatten and concatenate to get a 28672-dimensional vector.
        * Linear L2-SVM classifier trained over the feature vector.
    * 82.8% accuracy, outperforms K-means (80.6%)
* Street View House Number Classifier
    * Similar pipeline as CIFAR-10
    * 22.48% test error.
* The paper contains many examples of images generated by final and intermediate layers of the network.
* Images in the latent space do not show sharp transitions indicating that network did not memorize images.
* DCGAN can learn an interesting hierarchy of features.
* Networks seems to have some success in disentangling image representation from object representation.
* Vector arithmetic can be performed on the Z vectors corresponding to the face samples to get results like `smiling woman - normal woman + normal man = smiling man` visually.