4 papers · 1 filter
Interpretability Transfer from Language to Vision via Sparse Autoencoders
Alexey Kravets, Da Li, Chuan Li +2
Recent advances in language model interpretability using sparse autoencoders (SAEs) have yet to effectively translate to the visual domain, mainly due to the difficulty and ambigui…
Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting
Alexey Kravets, Da Chen, Vinay P. Namboodiri
CLIP is a foundational model with transferable classification performance in the few-shot setting. Several methods have shown improved performance of CLIP using few-shot examples.…
CLIP Adaptation by Intra-modal Overlap Reduction
Alexey Kravets, Vinay Namboodiri
Numerous methods have been proposed to adapt a pre-trained foundational CLIP model for few-shot classification. As CLIP is trained on a large corpus, it generalises well through ad…
Zero-Shot Class Unlearning in CLIP with Synthetic Samples
A. Kravets, V. Namboodiri
Machine unlearning is a crucial area of research. It is driven by the need to remove sensitive information from models to safeguard individuals' right to be forgotten under rigorou…