Conditional Generative Adversarial Nets Conditional Generative Adversarial Nets
Paper summary # Conditional Generative Adversarial Nets ## Introduction * Conditional version of [Generative Adversarial Nets (GAN)](https://gist.github.com/shagunsodhani/1f9dc0444142be8bd8a7404a226880eb) where both generator and discriminator are conditioned on some data **y** (class label or data from some other modality). * [Link to the paper](https://arxiv.org/abs/1411.1784) ## 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.
arxiv.org
scholar.google.com
Conditional Generative Adversarial Nets
Mirza, Mehdi and Osindero, Simon
arXiv e-Print archive - 2014 via Bibsonomy
Keywords: dblp


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