Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks

By Yoonho Boo, Sungho Shin, Jungwook Choi, Wonyong Sung. 2020

37 citations recorded by the source corpus.

The quantization of deep neural networks (QDNNs) has been actively studied for deployment in edge devices. Recent studies employ the knowledge distillation (KD) method to improve the performance of quantized networks. In this study, we propose stochastic precision ensemble training for QDNNs (SPEQ). SPEQ is a knowledge distillation training scheme; however, the teacher is formed by sharing the model parameters of the student network. We obtain the soft labels of the teacher by randomly changing the bit precision of the activation stochastically at each layer of the forward-pass computation. The student model is trained with these soft labels to reduce the activation quantization noise. The cosine similarity loss is employed, instead of the KL-divergence, for KD training. As the teacher model changes continuously by random bit-precision assignment, it exploits the effect of stochastic ensemble KD. SPEQ outperforms the existing quantization training methods in various tasks, such as image classification, question-answering, and transfer learning without the need for cumbersome teacher networks.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Knowledge Distillation: A Survey (2020, background): “Recently, unlike the quantized distillation methods above, a self-distillation training schemes is designed to improve the performance of quantized deep models, where teacher shares model parameters of student (Boo et al. 2021).”