  When considering post-training quantization, prior work has typically focused
on developing a mixed precision scheme or learning the best way to partition a
network for quantization. In our work, CPT-V, we look at a general way to
improve the accuracy of networks that have already been quantized, simply by
perturbing the quantization scales. Borrowing the idea of contrastive loss from
self-supervised learning, we find a robust way to jointly minimize a loss
function using just 1,000 calibration images. In order to determine the best
performing quantization scale, CPT-V contrasts the features of quantized and
full precision models in a self-supervised fashion.
  Unlike traditional reconstruction-based loss functions, the use of a
contrastive loss function not only rewards similarity between the quantized and
full precision outputs but also helps in distinguishing the quantized output
from other outputs within a given batch. In addition, in contrast to prior
works, CPT-V proposes a block-wise evolutionary search to minimize a global
contrastive loss objective, allowing for accuracy improvement of existing
vision transformer (ViT) quantization schemes. For example, CPT-V improves the
top-1 accuracy of a fully quantized ViT-Base by 10.30%, 0.78%, and 0.15% for
3-bit, 4-bit, and 8-bit weight quantization levels. Extensive experiments on a
variety of other ViT architectures further demonstrate its robustness in
extreme quantization scenarios. Our code is available at <link>.

Computer Vision and Pattern Recognition;Artificial Intelligence