# Efficient and Robust Quantization-aware Training via Adaptive Coreset Selection Source: [Efficient and Robust Quantization-aware Training via Adaptive Coreset Selection]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:11:37.234Z Authors: Xijie Huang, Zechun Liu, Shih-Yang Liu, Kwang-Ting Cheng Published: 2023-06-12T16:20:36.000Z ## Abstract Quantization-aware training (QAT) is a representative model compression method to reduce redundancy in weights and activations. However, most existing QAT methods require end-to-end training on the entire dataset, which suffers from long training time and high energy costs. In addition, the potential label noise in the training data undermines the robustness of QAT. We propose two metrics based on analysis of loss and gradient of quantized weights: error vector score and disagreement score, to quantify the importance of each sample during training. Guided by these two metrics, we proposed a quantization-aware Adaptive Coreset Selection (ACS) method to select the data for the current training epoch. We evaluate our method on various networks (ResNet-18, MobileNetV2, RetinaNet), datasets(CIFAR-10, CIFAR-100, ImageNet-1K, COCO), and under different quantization settings. Specifically, our method can achieve an accuracy of 68.39\\% of 4-bit quantized ResNet-18 on the ImageNet-1K dataset with only a 10\\% subset, which has an absolute gain of 4.24\\% compared to the baseline. Our method can also improve the robustness of QAT by removing noisy samples in the training set. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()