Vladimir Zaigrajew

Interpreting CLIP with Hierarchical Sparse Autoencoders

Sparse autoencoders (SAEs) are useful for detecting and steering interpretable features in neural networks, with particular potential for understanding complex multimodal representations. Given their ability to uncover interpretable features, SAEs are particularly valuable for analyzing vision-langu…