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sander.ai | ||
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distill.pub
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| | | | | What we'd like to find out about GANs that we don't know yet. | |
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yang-song.net
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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 ... | |
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jaketae.github.io
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| | | | | Note: This blog post was completed as part of Yale's CPSC 482: Current Topics in Applied Machine Learning. | |
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blog.evjang.com
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| | | This tutorial will show you how to use normalizing flows like MAF, IAF, and Real-NVP to deform an isotropic 2D Gaussian into a complex cl... | ||
