A Simple Generative Network
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Abstract (arXiv)
Generative neural networks are able to mimic intricate probability distributions such as those of handwritten text, natural images, etc. Since their inception several models were proposed. The most successful of these were based on adversarial (GAN), auto-encoding (VAE) and maximum mean discrepancy (MMD) relatively complex architectures and schemes. Surprisingly, a very simple architecture (a single feed-forward neural network) in conjunction with an obvious optimization goal (Kullback_Leibler divergence) was apparently overlooked. This paper demonstrates that such a model (denoted SGN for its simplicity) is able to generate samples visually and quantitatively competitive as compared with the fore-mentioned state of the art methods.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 20 references that are in the map. First posted on arXiv 17 Jun 2021.
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References · 11
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- 2013Deep Generative Stochastic Networks Trainable by Backpropcited by 36
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