Conditional Image Generation with PixelCNN Decoders Conditional Image Generation with PixelCNN Decoders
Paper summary * PixelRNN * PixelRNNs generate new images pixel by pixel (and row by row) via LSTMs (or other RNNs). * Each pixel is therefore conditioned on the previously generated pixels. * Training of PixelRNNs is slow due to the RNN-architecture (hard to parallelize). * Previously PixelCNNs have been suggested, which use masked convolutions during training (instead of RNNs), but their image quality was worse. * They suggest changes to PixelCNNs that improve the quality of the generated images (while still keeping them faster than RNNs). ### How * PixelRNNs split up the distribution `p(image)` into many conditional probabilities, one per pixel, each conditioned on all previous pixels: `p(image) = <product> p(pixel i | pixel 1, pixel 2, ..., pixel i-1)`. * PixelCNNs implement that using convolutions, which are faster to train than RNNs. * These convolutions uses masked filters, i.e. the center weight and also all weights right and/or below the center pixel are `0` (because they are current/future values and we only want to condition on the past). * In most generative models, several layers are stacked, ultimately ending in three float values per pixel (RGB images, one value for grayscale images). PixelRNNs (including this implementation) traditionally end in a softmax over 255 values per pixel and channel (so `3*255` per RGB pixel). * The following image shows the application of such a convolution with the softmax output (left) and the mask for a filter (right): * ![Masked convolution]( "Masked convolution") * Blind spot * Using the mask on each convolutional filter effectively converts them into non-squared shapes (the green values in the image). * Advantage: Using such non-squared convolutions prevents future values from leaking into present values. * Disadvantage: Using such non-squared convolutions creates blind spots, i.e. for each pixel, some past values (diagonally top-right from it) cannot influence the value of that pixel. * ![Blind spot]( "Blind Spot") * They combine horizontal (1xN) and vertical (Nx1) convolutions to prevent that. * Gated convolutions * PixelRNNs via LSTMs so far created visually better images than PixelCNNs. * They assume that one advantage of LSTMs is, that they (also) have multiplicative gates, while stacked convolutional layers only operate with summations. * They alleviate that problem by adding gates to their convolutions: * Equation: `output image = tanh(weights_1 * image) <element-wise product> sigmoid(weights_2 * image)` * `*` is the convolutional operator. * `tanh(weights_1 * image)` is a classical convolution with tanh activation function. * `sigmoid(weights_2 * image)` are the gate values (0 = gate closed, 1 = gate open). * `weights_1` and `weights_2` are learned. * Conditional PixelCNNs * When generating images, they do not only want to condition the previous values, but also on a laten vector `h` that describes the image to generate. * The new image distribution becomes: `p(image) = <product> p(pixel i | pixel 1, pixel 2, ..., pixel i-1, h)`. * To implement that, they simply modify the previously mentioned gated convolution, adding `h` to it: * Equation: `output image = tanh(weights_1 * image + weights_2 . h) <element-wise product> sigmoid(weights_3 * image + weights_4 . h)` * `.` denotes here the matrix-vector multiplication. * PixelCNN Autoencoder * The decoder in a standard autoencoder can be replaced by a PixelCNN, creating a PixelCNN-Autoencoder. ### Results * They achieve similar NLL-results as PixelRNN on CIFAR-10 and ImageNet, while training about twice as fast. * Here, "fast" means that they used 32 GPUs for 60 hours. * Using Conditional PixelCNNs on ImageNet (i.e. adding class information to each convolution) did not improve the NLL-score, but it did improve the image quality. * ![ImageNet]( "ImageNet") * They use a different neural network to create embeddings of human faces. Then they generate new faces based on these embeddings via PixelCNN. * ![Portraits]( "Portraits") * Their PixelCNN-Autoencoder generates significantly sharper (i.e. less blurry) images than a "normal" autoencoder.
Conditional Image Generation with PixelCNN Decoders
Aaron van den Oord and Nal Kalchbrenner and Oriol Vinyals and Lasse Espeholt and Alex Graves and Koray Kavukcuoglu
arXiv e-Print archive - 2016 via Local arXiv
Keywords: cs.CV, cs.LG


Summary by Shagun Sodhani 4 years ago
Your comment:
Summary by Alexander Jung 3 years ago
Your comment: allows researchers to publish paper summaries that are voted on and ranked!

Sponsored by: and