3 papers
cs.CV2026
Suppressing Non-Semantic Noise in Masked Image Modeling Representations
Martine Hjelkrem-Tan, Marius Aasan, Rwiddhi Chakraborty +3
Masked Image Modeling (MIM) has become a ubiquitous self-supervised vision paradigm. In this work, we show that MIM objectives cause the learned representations to retain non-seman…
cs.LG2026
Why Prototypes Collapse: Diagnosing and Preventing Partial Collapse in Prototypical Self-Supervised Learning
Gabriel Y. Arteaga, Marius Aasan, Rwiddhi Chakraborty +4
Prototypical self-supervised learning methods consistently suffer from partial prototype collapse, where multiple prototypes converge to nearly identical representations. This unde…
cs.CV2024
Visual Data Diagnosis and Debiasing with Concept Graphs
Rwiddhi Chakraborty, Yinong Wang, Jialu Gao +3
The widespread success of deep learning models today is owed to the curation of extensive datasets significant in size and complexity. However, such models frequently pick up inher…