Web: http://arxiv.org/abs/2204.04215

June 23, 2022, 1:13 a.m. | Yefei He, Luoming Zhang, Weijia Wu, Hong Zhou

cs.CV updates on arXiv.org arxiv.org

Data-free quantization is a task that compresses the neural network to low
bit-width without access to original training data. Most existing data-free
quantization methods cause severe performance degradation due to inaccurate
activation clipping range and quantization error, especially for low bit-width.
In this paper, we present a simple yet effective data-free quantization method
with accurate activation clipping and adaptive batch normalization. Accurate
activation clipping (AAC) improves the model accuracy by exploiting accurate
activation information from the full-precision model. Adaptive batch …

arxiv data free lg quantization

More from arxiv.org / cs.CV updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY