Training-free Acceleration for Blind Compressed Image Restoration via Activation-Calibrated Singular Value Decomposition

Key points

• We study training-free acceleration for such models through an empirical, operator-level analysis.1
• Specifically, we profile where computation is concentrated, characterize channel-wise activation distributions under compressed inputs, and examine the singular-value decay of representative projection weights.1
• They further establish truncated SVD (TSVD) as a principled training-free baseline for blind CIR and reveal why a uniform low-rank objective is mismatched to the channel-imbalanced feature distributions of compressed images.1
• Accordingly, we formulate Activation-Calibrated Singular Value Decomposition (ASVD) for this restoration setting, deriving low-rank factors entirely offline from pretrained weights and lightweight activation statistics gathered from only a few compressed calibration images.1

Sources (1)

• [1]Training-free Acceleration for Blind Compressed Image Restoration via Activation-Calibrated Singular Value Decomposition
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 04:16 PM

  Transformer-based models achieve strong performance on blind compressed image restoration (CIR) under unknown compression settings, but their densely stacked projection operators make deployment costly.

  We study training-free acceleration for such models through an empirical, operator-level analysis.

Extractive summary: sentences quoted from the sources.