Topic: Generative Models

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  1. Scaling Laws for Autoregressive Generative Modeling

    Paper • Oct 28, 2020 • arXiv • Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B. Brown, Prafulla Dhariwal, Scott Gray, Chris Hallacy, Benjamin Mann, Alec Radford, Aditya Ramesh, Nick Ryder, Daniel M. Ziegler, John Schulman, Dario Amodei, Sam McCandlish

    Building upon previous work establishing scaling laws for language models, we investigate whether similar power-law scaling relationships hold across other data modalities. We train autoregressive ...

  2. Large Scale GAN Training for High Fidelity Natural Image Synthesis

    Paper • Sep 28, 2018 • arXiv • Andrew Brock, Jeff Donahue, Karen Simonyan

    Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train ...

  3. Neural Discrete Representation Learning

    Paper • Nov 2, 2017 • arXiv • Aaron van den Oord, Oriol Vinyals, Koray Kavukcuoglu

    Learning useful representations without supervision remains a key challenge in machine learning. We propose the Vector Quantised-Variational AutoEncoder (VQ-VAE), a simple yet powerful generative m...

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