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Learning to Cluster. A deep clustering strategy.
The main features of gt-ripl/l2c are: General Adaptation Techniques.
Projects with overlapping indexed features include: bit-da/dda — Code release for "Dynamic Domain Adaptation for Efficient Inference" (CVPR 2021). bit-da/tsa — [CVPR 2021 Oral] Code release for "Transferable Semantic Augmentation for Domain Adaptation". corenel/pytorch-atda — A PyTorch implementation for Asymmetric Tri-training for Unsupervised Domain Adaptation. cuishuhao/bnm — code of Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations… enesdoruk/transadapter — TransAdapter: Vision Transformer for Feature-Centric Unsupervised Domain Adaptation. astuti/ila-da — More Details coming soon. If you have any questions regarding our code, please contact asharma@eng.ucsd.edu ILA-DA…
Code release for "Dynamic Domain Adaptation for Efficient Inference" (CVPR 2021)
CVPR 2021 Oral Code release for "Transferable Semantic Augmentation for Domain Adaptation"
A PyTorch implementation for Asymmetric Tri-training for Unsupervised Domain Adaptation
More Details coming soon. If you have any questions regarding our code, please contact asharma@eng.ucsd.edu ILA-DA CVPR2021