IC-627ResNet 18 and ViT B/16 retain significant ImageNet accuracy at 2–3 bit weight compression via JLCM

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Edouard YVINEC, Arnaud Dapogny, Kevin Bailly

SourceNetwork Memory Footprint Compression Through Jointly Learnable Codebooks and Mappings

The paper measures ImageNet top-1 accuracy of ResNet 18 and ViT B/16 after compressing their weights with JLCM at two compression rates. At 5.33x compression (approximately 3 bits), ResNet 18 achieves 62.939% and ViT B/16 achieves 80.558%. At 7.5x compression (approximately 2 bits), ResNet 18 drops to 41.779% and ViT B/16 to 69.996%. The authors report that JLCM reaches over 99% of the original ViT B/16 accuracy in its data-driven variant at 5.33x.

Evidence
correlational
Key metric
ResNet 18: 62.939% at 5.33x, 41.779% at 7.5x; ViT B/16: 80.558% at 5.33x, 69.996% at 7.5x
Caveat
Results depend on the calibration set size and the specific JLCM hyperparameters; the data-free initialization alone performs substantially worse than the full data-driven JLCM.
Model
ResNet / ResNet-152 / ResNet-101 / ResNet-50-BN ResNet-18, ViT ViT-B/16
Datasets
ImageNet-1k / ImageNet / ImageNet-1k-val / ImageNet-Val [eval]
Methods
NUPES [compared-to], BRECQ [compared-to], ADRound [compared-to], PowerQuant [compared-to], RED++ [compared-to], SQuant [compared-to], DFQ [compared-to]
Related work
NUPES [compared-to], BRECQ [compared-to], PowerQuant [compared-to], RED++ [compared-to]
Related findings
IC-626
Extraction
automatic-extraction