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        "author": "Ethan Harris",
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        "description": "# \\[WIP\\] torchbearer.variational\nA Variational Auto-Encoder library for PyTorch with torchbearer\n\n## Contents\n- [About](#about)\n- [Installation](#installation)\n- [Goals](#goals)\n\n<a name=\"about\"/>\n\n## About\n\nTorchbearer.variational is a companion package to [torchbearer](https://github.com/ecs-vlc/torchbearer) which is intended to\nre-implement state of the art models and practices relating to the world of Variational Auto-Encoders (VAEs). The goal\nis to provide everything from useful abstractions to complete re-implementations of papers. This is in order to support\nboth research and teaching / learning regarding VAEs.\n\n<a name=\"installation\"/>\n\n## Installation\n\nTBC\n\n<a name=\"goals\"/>\n\n## Goals\n\nCurrently, _variational_ only includes abstractions for simple VAEs and some accompaniments, the next steps are as follows:\n\n- Construct some separate part of the docs for the _variational_ content\n- Implement a series of standard models with associated notes pages and example usages\n- Implement other divergences not in PyTorch such as MMD, Jensen-Shannon, etc.\n- Implement and document tools for sampling the latent spaces of models and producing figures\n- Implement other dataloaders not in torchvision and add associated docs\n\n",
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        "license": "MIT",
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        "name": "torchbearer-variational",
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