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Code for ``NIPS 2021 universal semi-supervised learning''
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zhuohuangai/cafa-1
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# Univsersal Semi-Supervised Learning This repository is reimplementation of [realistic-ssl-evaluation-pytorch] Here is the original repo: https://github.com/perrying/realistic-ssl-evaluation-pytorch # Requirements - Python 3.6+ - PyTorch 1.1.0 - torchvision 0.3.0 - numpy 1.16.2 # How to run 1)download the visda2017 dataset to the corresponding path: ```./data/visda/``` 2)run the following command ```python train_cafa.py -a [backbone method] -d [dataset] -tp [dataset settings]``` Default backbone method setting is PI. Please check the options by ```python train.py -h``` # Performance visda dataset under subset mismatch using PI with one random run: 0.8805522322654724 visda dataset under intersectional mismatch using PI with one random run: 0.8585433959960938
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Code for ``NIPS 2021 universal semi-supervised learning''
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