SQWA: Stochastic Quantized Weight Averaging For Improving The Generalization Capability Of Low-Precision Deep Neural Networks

Abstract

Low-precision deep neural networks (DNNs) are very needed for efficient implementations, but severe quantization of weights often sacrifices the generalization capability and lowers the test accuracy. We present a new quantized neural network optimization approach, stochastic quantized weight averaging (SQWA), to design low-precision DNNs with good generalization capability using model averaging. The proposed approach includes (1) floating-point model training, (2) direct quantization of weights, (3) capturing multiple low precision models during retraining with cyclical learning rates, (4) averaging the captured models, and (5) re-quantizing the averaged model and fine-tuning it with low-learning rates. With SQWA training, we could develop the best performing QDNNs for image classification on ImageNet datasets and also for semantic segmentation on Pascal VOC 2012 dataset.