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ViT depth computation approximated by linear dynamics

By PulseAugur Editorial · [1 sources] ·

Researchers have explored the internal computations of Vision Transformers (ViTs) by applying Dynamic Mode Decomposition (DMD). Their findings suggest that contiguous blocks within a ViT can be approximated by a single linear operator applied repeatedly. This linear operator accurately predicts intermediate activations over short spans, particularly in earlier layers and for the 'cls' token, but this local fidelity does not translate to improved performance on downstream tasks. AI

IMPACT Reveals that ViT computations exhibit linear dynamics, suggesting potential for model compression and efficiency gains.

RANK_REASON The cluster contains an academic paper detailing a new analytical method for understanding existing models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

ViT depth computation approximated by linear dynamics

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The cluster contains an academic paper detailing a new analytical method for understanding existing models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Saif Eddin Jabari ·

    Dynamic Mode Decomposition along Depth in Vision Transformers

    Recent work has shown that contiguous vision transformer (ViT) blocks (a) can be replaced by a linear map and (b) organize into recurrent phases of computation. We ask whether these observations coincide: does ViT depth implement approximately \textit{autonomous linear} dynamics,…

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