Search Results for author:
Found 2 papers, 1 papers with code
Adapting Self-Supervised Representations to Multi-Domain Setups
Current state-of-the-art self-supervised approaches, are effective when trained on individual domains but show limited generalization on unseen domains.
Identifying Interpretable Subspaces in Image Representations
Although many existing approaches interpret features independently, we observe in state-of-the-art self-supervised and supervised models, that less than 20% of the representation space can be explained by individual features.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
