The Independence Prior of SAEs Fragments Visual Concepts

Sparse Autoencoders (SAEs) decompose model activations into sparse combinations of interpretable dictionary atoms. Although SAEs are grounded in the Linear Representation Hypothesis (LRH), their objective smuggles in an additional prior: concepts across patches are treated as…

Tommaso Mencattini, Giorgos Nikolaou, Donato Crisostomi, Thomas Fel, Francesco Montagna, Emanuele Rodol\`a, Francesco Locatello·arXiv — cs.AI

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Key Takeaways

01.Sparse Autoencoders (SAEs) decompose model activations into sparse combinations of interpretable dictionary atoms
02.Although SAEs are grounded in the Linear Representation Hypothesis (LRH), their objective smuggles in an additional prior: concepts across patches are treated as…

This material is provided as an extended analytical summary.

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