A Collection of Variational Autoencoders (VAE) in PyTorch.
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Updated
Jun 13, 2024 - Python
A Collection of Variational Autoencoders (VAE) in PyTorch.
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
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Collection of popular and reproducible image denoising works.
FMA: A Dataset For Music Analysis
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
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Insight Toolkit (ITK) -- Official Repository. ITK builds on a proven, spatially-oriented architecture for processing, segmentation, and registration of scientific images in two, three, or more dimensions.
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Presentation-Ready Data Summary and Analytic Result Tables
High-fidelity performance metrics for generative models in PyTorch
This is the repository of our article published in RecSys 2019 "Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches" and of several follow-up studies.
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