<ul><li>A recent study analyzes the phenomenon of residual specialization in transformer networks, particularly in vision transformers.</li><li>The study links the specialization of residual contributions to the low-dimensional structure of visual head representations.</li><li>The authors examine the effect of head specialization on multimodal models and its impact on zero-shot classification performance.</li><li>The study introduces ResiDual, a technique for spectral alignment of the residual stream, which demonstrates fine-tuning level performance on different data distributions.</li></ul>

ResiDual Transformer Alignment with Spectral Decomposition

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