4 papers · 1 filter
Interpretability-Guided Soft Pruning of Attention Heads in Vision Transformers
Kamil Książek, Piotr Suszyński, Michał Jan Włodarczyk +2
Vision foundation models, such as DINOv2, learn highly expressive representations but rely on massive, opaque architectures that demand substantial computational power and memory.…
Your CLIP has 164 dimensions of noise: Exploring the embeddings covariance eigenspectrum of contrastively pretrained vision-language transformers
Jakub Grzywaczewski, Dawid Płudowski, Przemysław Biecek
Contrastively pre-trained Vision-Language Models (VLMs) serve as powerful feature extractors. Yet, their shared latent spaces are prone to structural anomalies and act as repositor…
LINE: LLM-based Iterative Neuron Explanations for Vision Models
Vladimir Zaigrajew, Michał Piechota, Gaspar Sekula +2
Interpreting individual neurons in deep neural networks is a crucial step towards understanding their complex decision-making processes and ensuring AI safety. Despite recent progr…
SwordBench: Evaluating Orthogonality of Steering Image Representations
Vladimir Zaigrajew, Dawid Pludowski, Hubert Baniecki +1
Steering or intervening on model representations at inference time to correct predictions is essential for AI interpretability and safety, yet existing evaluation protocols are lim…