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 sander.ai
The paradox of diffusion distillation - Sander Dieleman
| | yang-song.net
Generative Modeling by Estimating Gradients of the Data Distribution | Yang Song
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| | This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood ...
| | distill.pub
Open Questions about Generative Adversarial Networks
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| | What we'd like to find out about GANs that we don't know yet.
| | www.v7labs.com
The Complete Guide to Generative Adversarial Networks [GANs]
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| | What are Generative Adversarial Networks and how do they work? Learn about GANs architecture and model training, and explore the most popular generative models variants and their limitations.
| | kyunghyuncho.me
Stochastic variational inference for low-rank stochastic block models, or how i re-discovered SBM unnecessarily - Kyunghyun Cho
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