Summary of Clamp-vit: Contrastive Data-free Learning For Adaptive Post-training Quantization Of Vits, by Akshat Ramachandran et al.
CLAMP-ViT: Contrastive Data-Free Learning for Adaptive Post-Training Quantization of ViTs
by Akshat Ramachandran, Souvik Kundu, Tushar Krishna
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces CLAMP-ViT, a novel data-free post-training quantization method for vision transformers (ViTs). Unlike previous techniques, CLAMP-ViT leverages inter-patch relationships to generate richer, semantically meaningful data, improving quantization accuracy. The method employs a two-stage approach combining patch-level contrastive learning and layer-wise evolutionary search to identify optimal quantization parameters. Evaluations across various vision tasks demonstrate the superiority of CLAMP-ViT, achieving performance improvements of up to 3% in top-1 accuracy for classification, 0.6 mAP for object detection, and 1.5 mIoU for segmentation at similar or better compression ratio over existing alternatives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CLAMP-ViT is a new way to make computers work faster without losing any information. It’s especially useful for artificial intelligence models that do things like recognize pictures or detect objects. The old way of doing this made the models very big and used too much energy, so scientists came up with CLAMP-ViT. This method uses patterns in the data to create new, more accurate versions of the model, which can then be shrunk down without losing any accuracy. In tests, CLAMP-ViT was able to make models that were 3% better at recognizing pictures and 0.6% better at detecting objects, all while using less energy. |
Keywords
» Artificial intelligence » Classification » Object detection » Quantization » Vit
