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jaketae.github.io | ||
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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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lilianweng.github.io
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| | | | | [Updated on 2021-09-19: Highly recommend this blog post on score-based generative modeling by Yang Song (author of several key papers in the references)]. [Updated on 2022-08-27: Added classifier-free guidance, GLIDE, unCLIP and Imagen. [Updated on 2022-08-31: Added latent diffusion model. [Updated on 2024-04-13: Added progressive distillation, consistency models, and the Model Architecture section. | |
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resources.paperdigest.org
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| | | | | The Conference on Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world. Paper Digest Team analyze all papers published on NIPS in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically construc | |
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n9o.xyz
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| | | In the last few years, the hype around artificial intelligence has been increasing (again). Most of it is due to truly groundbreaking research and innovative showcases in the field. From machines winning complex games like Go and Dota 2, to various content generation techniques, these technologies will impact our future. | ||
