The authors studied post-training quantized vision transformers under corruptions and domain shifts.
Recalibrating activations helps quantized vision transformers withstand distribution shifts
QuAR aligns test-time activation statistics with frozen quantizer ranges in one forward pass, without gradients or parameter updates.
Big Tech
Hyeongheon Cha · Young D. Kwon · Sung-Ju Lee
KAIST · Samsung AI Center-Cambridge · University of Cambridge
Research Digest··3 min read
Cha, Kwon and Lee identify a quantization-specific weakness under distribution shift: channel-level activation statistics drift away from the ranges used to calibrate frozen quantizers, distorting how activations map to discrete codes.
Why this paper
From Samsung AI Center-Cambridge and 2 others
In one line
QuAR recalibrates activations at frozen quantizer inputs to correct distribution shift, achieving top accuracy among backprop-free TTA methods across bit widths.
What we could check
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- ✓Limitations stated by the authors (2 noted)
- ✓Reports numbers on named benchmarks (2 benchmarks)
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