Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization

• URL: http://arxiv.org/abs/2204.04215v1
• Date: Fri, 8 Apr 2022 01:56:51 GMT
• Title: Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization
• Authors: Yefei He, Luoming Zhang, Weijia Wu, Hong Zhou
• Abstract summary: We present a data-free quantization method with accurate activation clipping and adaptive batch normalization. Experiments demonstrate that the proposed data-free quantization method can yield surprisingly performance, achieving 64.33% top-1 accuracy of ResNet18 on ImageNet dataset.
• Score: 4.329951775163721
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 normalization firstly proposes to address the quantization error from distribution changes by updating the batch normalization layer adaptively. Extensive experiments demonstrate that the proposed data-free quantization method can yield surprisingly performance, achieving 64.33% top-1 accuracy of ResNet18 on ImageNet dataset, with 3.7% absolute improvement outperforming the existing state-of-the-art methods.

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