# 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.