[R] SlimGANs: Real-Time Adjustable Model Sizes

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Generative adversarial networks (GANs) have been very successful with their increasingly larger model scales in recent years. GANs’ practical applications, however, are also becoming increasingly restricted by the unwieldy model sizes. Researchers from University of Chinese Academy of Sciences, ByteDance AI Lab, Inception Institute of Artificial Intelligence and Beihang University recently proposed novel “once-for-all” slimmable generative adversarial networks (SlimGANs) that can easily change model sizes during runtime to implement quality-efficiency trade-offs based on practical needs.

Here is a quick read: SlimGANs: Real-Time Adjustable Model Sizes

The paper Slimmable Generative Adversarial Networks has been accepted by AAAI 2021 and is available on arXiv. The codes are on the project GitHub.

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