Using a relative-entropy coreset and layer-wise feature alignment, QuaRC improves 2-bit quantization-aware training accuracy by up to 5.72 points on ImageNet-1K when training on 1% of the data.
Energy efficient federated learning over heterogeneous mobile devices via joint design of weight quantization and wireless transmission,
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Enhancing Quantization-Aware Training on Edge Devices via Relative Entropy Coreset Selection and Cascaded Layer Correction
Using a relative-entropy coreset and layer-wise feature alignment, QuaRC improves 2-bit quantization-aware training accuracy by up to 5.72 points on ImageNet-1K when training on 1% of the data.