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Steering CLIP's vision transformer with sparse autoencoders
This study explores sparse autoencoders in vision models, revealing unique processing patterns and enhancing steerability, leading to improved performance in vision disentanglement tasks and defense strategies.
https://arxiv.org/abs//2504.08729
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
This study explores sparse autoencoders in vision models, revealing unique processing patterns and enhancing steerability, leading to improved performance in vision disentanglement tasks and defense strategies.
https://arxiv.org/abs//2504.08729
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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