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Found 4 papers, 1 papers with code
Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance
Even more unconventionally, our proposed method enables directly training sparse unbalanced GANs with an extremely sparse generator from scratch.
DGL-GAN: Discriminator Guided Learning for GAN Compression
Moreover, DGL-GAN is also effective in boosting the performance of original uncompressed GANs.
Sparse Unbalanced GAN Training with In-Time Over-Parameterization
Perhaps most importantly, we find instead of inheriting parameters from expensive pre-trained GANs, directly training sparse GANs from scratch can be a much more efficient solution.
AlphaGAN: Fully Differentiable Architecture Search for Generative Adversarial Networks
To this end, we propose a fully differentiable search framework for generative adversarial networks, dubbed alphaGAN.
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