Finding the Task-Optimal Low-Bit Sub-Distribution in Deep Neural Networks. (arXiv:2112.15139v2 [cs.CV] UPDATED)

Quantized neural networks typically require smaller memory footprints and
lower computation complexity, which is crucial for efficient deployment.
However, quantization inevitably leads to a distribution divergence from the
original network, which generally degrades the performance. To tackle this
issue, massive efforts have been made, but most existing approaches lack
statistical considerations and depend on several manual configurations. In this
paper, we present an adaptive-mapping quantization method to learn an optimal
latent sub-distribution that is inherent within models and smoothly
approximated …

arxiv cv distribution networks neural networks