Abstract
Abstract Generative Adversarial Nets (GANs) are currently the dominant model for high fidelity image synthesis. GANs suffer from two major drawbacks: complicated dynamics and the requirement for an auxiliary network for training (discriminator). However, if we train a decoder-only network we circumvent both drawbacks. To achieve that, the decoder should capture high-order correlations that exist between the variables. We demonstrate this is possible by designing a high-order polynomial generator using tensorial factors. We implement two variants of the model, which we call NAPS. We experiment with both MNIST and CelebA and showcase that our model captures the data distribution and synthesizes new images with significantly less parameters than the corresponding baseline.
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