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sander.ai | ||
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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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angusturner.github.io
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| | | | | Machine Learning and Data Science. | |
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bartwronski.com
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| | | | | Recently, numerous academic papers in the machine learning / computer vision / image processing domains (re)introduce and discuss a "frequency loss function" or "spectral loss" - and while for many it makes sense and nicely improves achieved results, some of them define or use it wrongly. The basic idea is - instead of comparing pixels... | |
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morepypy.blogspot.com
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| | | String formatting is probably something you do just about every day in Python, and never think about. It's so easy, just "%d %d" % (i, i) ... | ||
