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disent
π§Ά Modular VAE disentanglement framework for python built with PyTorch Lightning βΈ Including metrics and datasets βΈ With strongly supervised, weakly supervised and unsupervised methods βΈ Easily configured and run with Hydra config βΈ Inspired by disentanglement_liblightning-nca
π¦ β‘οΈ Neural Cellular Automata (NCA) implemented with PyTorch Lightningeunomia
π A sane but flexible configuration framework inspired by Hydra config, with yaml and pythonic backends.stretched
ππ Python flexbox layout engine bindings for Stretch πdoorway
πͺ Essential utilities for working with files. Including easy downloading, fast hashing, stale file detection, dataset sharding, proxy downloading, file renamingrldm-2022-sequential-states
β±π Additional resources for "Accounting for the Sequential Nature of States to Learn Features for Reinforcement Learning" submitted to RLDM 2022msc-research
π Repository containing all the research for my MSc. "Disentanglement Using VAEs Resembles Distance Learning and Requires Overlapping Data" π This repo is an old fork of the disent framework that contains the original research code in its final state!Love Open Source and this site? Check out how you can help us