6 papers
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…
The Metagame of Interpretability and Meta-Attributions
Hubert Baniecki, Przemyslaw Biecek, Fabian Fumagalli
How can an arbitrary attribution method be generalized from first principles to capture interactions? We answer this with the metagame, a conceptual framework for quantifying secon…
Exploring Local Explanations of Nonlinear Models Using Animated Linear Projections
Nicholas Spyrison, Dianne Cook, Przemyslaw Biecek
The increased predictive power of machine learning models comes at the cost of increased complexity and loss of interpretability, particularly in comparison to parametric statistic…