Quantization-aware training (QAT) and Knowledge Distillation (KD) are combined to achieve competitive performance in creating low-bit deep learning models. However, existing works applying KD to QAT require tedious hyper-parameter tuning to balance the weights of different loss terms, assume the ...
知识蒸馏(Knowledge distillation) 量化(quantization) 量化(quantization)是模型压缩的一种常用方法,通常情况下可以使用不同的量化策略,将深度学习模型参数与运算的精度从浮点数(FP32)降低至较低的精度,如INT8,一方面可以提升模型在cpu/gpu等硬件的推理计算效率,减少计算成本,另一方面能够减小模型的size,在边缘设备具有...
Then we fine-tune the quantized student network with the full-precision teacher network and the generated images by utilizing knowledge distillation (KD). The proposed DFQF outperforms state-of-theart post-train quantization methods, and achieve W4...
Data-Free Network Quantization With Adversarial Knowledge Distillation 1. Introduction 在本文中,我们提出了一个对抗性知识提炼框架,在无法获得原始训练数据的损失时,通过对抗性学习使最坏情况下的可能损失(最大损失)最小化。与[36]的关键区别在于,给定任何元数据,我们利用它们来约束对抗性学习框架中的发生器。为了...