Beyond SAEs: Consistent Concept Discovery with Sparse Coding
Replaces the linear SAE encoder with per-sample sparse coding to learn dictionaries that stay consistent across training runs.
jinho-choi
Replaces the linear SAE encoder with per-sample sparse coding to learn dictionaries that stay consistent across training runs.
Grounding visual concepts in large vision-language models via a attention-based linking mechanism.
Characterizes dataset bias by disentangling visual concepts learned by sparse autoencoders on vision models.