Introspective Generative Modeling: Decide Discriminatively Introspective Generative Modeling: Decide Discriminatively
Paper summary In this work they take a different approach to the GAN model \cite{1406.2661}. In the traditionally GAN model a neural network is trained to up-sample from random noise in a feed forward fashion to generate samples from the data distribution. This work instead iteratively permutes an image of random noise similar to Artistic Style Transfer \cite{1508.06576}. The image is permuted in order to fool a set of discriminators. To obtain the set of discriminators each is trained starting from random noise until some max $t$ step. 1. At first a discriminator is trained to discriminate between the true data and random noise . 2. Images is then permuted using gradients which aim to fool the discriminator and included in the data distribution as a negative example. 3. The discriminator is trained on the true data + random noise + fake data from the previous steps The images generated at each step are shown below: After being trained the model is able to generate a sample by iterating over each trained discriminator and applying gradient updates on from random noise. For this storing only the weights of the discriminators is required. Poster from ICCV2017:
Introspective Generative Modeling: Decide Discriminatively
Justin Lazarow and Long Jin and Zhuowen Tu
arXiv e-Print archive - 2017 via Local arXiv
Keywords: cs.CV, cs.LG, cs.NE


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