Software & Data Downloads — SOCKET
SOurce-free Cross-modal KnowledgE Transfer for transfering knowledge from neural networks trained on a source sensor modality.
SOCKET allows transferring knowledge from neural networks trained on a source sensor modality (such as RGB) for one or more domains where large amount of annotated data may be available to an unannotated target dataset from a different sensor modality (such as infrared or depth). It makes use of task-irrelevant paired source-target images in order to promote feature alignment between the two modalities as well as distribution matching between the source batch norm features (mean and variance) and the target features.
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Related Publications
- , "Cross-Modal Knowledge Transfer Without Task-Relevant Source Data", European Conference on Computer Vision (ECCV), Avidan, S and Brostow, G and Cisse M and Farinella, G.M. and Hassner T., Eds., DOI: 10.1007/978-3-031-19830-4_7, October 2022, pp. 111-127.
BibTeX TR2022-135 PDF Video Software Presentation- @inproceedings{Ahmed2022oct,
- author = {{Ahmed, Sk Miraj and Lohit, Suhas and Peng, Kuan-Chuan and Jones, Michael J. and Roy Chowdhury, Amit K.}},
- title = {Cross-Modal Knowledge Transfer Without Task-Relevant Source Data},
- booktitle = {European Conference on Computer Vision (ECCV)},
- year = 2022,
- editor = {Avidan, S and Brostow, G and Cisse M and Farinella, G.M. and Hassner T.},
- pages = {111--127},
- month = oct,
- publisher = {Springer},
- doi = {10.1007/978-3-031-19830-4_7},
- isbn = {978-3-031-19830-4},
- url = {https://www.merl.com/publications/TR2022-135}
- }
- , "Cross-Modal Knowledge Transfer Without Task-Relevant Source Data", European Conference on Computer Vision (ECCV), Avidan, S and Brostow, G and Cisse M and Farinella, G.M. and Hassner T., Eds., DOI: 10.1007/978-3-031-19830-4_7, October 2022, pp. 111-127.
Software & Data Downloads
Access software at https://github.com/merlresearch/SOCKET.