Conditional Generative Adversarial Nets Conditional Generative Adversarial Nets
Paper summary # Conditional Generative Adversarial Nets ## Introduction * Conditional version of [Generative Adversarial Nets (GAN)]( where both generator and discriminator are conditioned on some data **y** (class label or data from some other modality). * [Link to the paper]( ## Architecture * Feed **y** into both the generator and discriminator as additional input layers such that **y** and input are combined in a joint hidden representation. ## Experiment ### Unimodal Setting * Conditioning MNIST images on class labels. * *z* (random noise) and **y** mapped to hidden layers with ReLu with layer sizes of 200 and 1000 respectively and are combined to obtain ReLu layer of dimensionality 1200. * Discriminator maps *x* (input) and **y** to maxout layers and the joint maxout layer is fed to sigmoid layer. * Results do not outperform the state-of-the-art results but do provide a proof-of-the-concept. ### Multimodal Setting * Map images (from Flickr) to labels (or user tags) to obtain the one-to-many mapping. * Extract image and text features using convolutional and language model. * Generative Model * Map noise and convolutional features to a single 200 dimensional representation. * Discriminator Model * Combine the representation of word vectors (corresponding to tags) and images. ## Future Work * While the results are not so good, they do show the potential of Conditional GANs, especially in the multimodal setting.
Conditional Generative Adversarial Nets
Mirza, Mehdi and Osindero, Simon
arXiv e-Print archive - 2014 via Bibsonomy
Keywords: dblp

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