Progressive growing stabilizes GAN training to produce high-resolution images of unprecedented quality and achieves a record unsupervised inception score of 8.80 on CIFAR10.
Kingma and Max Welling
2 Pith papers cite this work. Polarity classification is still indexing.
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MetaSymbO proposes a three-agent framework with symbolic latent evolution that improves structural validity and language alignment for metamaterial design from free-form text intents.
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Progressive Growing of GANs for Improved Quality, Stability, and Variation
Progressive growing stabilizes GAN training to produce high-resolution images of unprecedented quality and achieves a record unsupervised inception score of 8.80 on CIFAR10.
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METASYMBO: Multi-Agent Language-Guided Metamaterial Discovery via Symbolic Latent Evolution
MetaSymbO proposes a three-agent framework with symbolic latent evolution that improves structural validity and language alignment for metamaterial design from free-form text intents.