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ZeroQ: A Novel Zero Shot Quantization Framework
[article]
2020
arXiv
pre-print
Quantization is a promising approach for reducing the inference time and memory footprint of neural networks. However, most existing quantization methods require access to the original training dataset for retraining during quantization. This is often not possible for applications with sensitive or proprietary data, e.g., due to privacy and security concerns. Existing zero-shot quantization methods use different heuristics to address this, but they result in poor performance, especially when
arXiv:2001.00281v1
fatcat:iyheue7fybfyxpqhrou2hkdpsq