Annealing Knowledge Distillation

• URL: http://arxiv.org/abs/2104.07163v1
• Date: Wed, 14 Apr 2021 23:45:03 GMT
• Title: Annealing Knowledge Distillation
• Authors: Aref Jafari, Mehdi Rezagholizadeh, Pranav Sharma, Ali Ghodsi
• Abstract summary: We propose an improved knowledge distillation method (called Annealing-KD) by feeding the rich information provided by the teacher's soft-targets incrementally and more efficiently. This paper includes theoretical and empirical evidence as well as practical experiments to support the effectiveness of our Annealing-KD method.
• Score: 5.396407687999048
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Significant memory and computational requirements of large deep neural networks restrict their application on edge devices. Knowledge distillation (KD) is a prominent model compression technique for deep neural networks in which the knowledge of a trained large teacher model is transferred to a smaller student model. The success of knowledge distillation is mainly attributed to its training objective function, which exploits the soft-target information (also known as "dark knowledge") besides the given regular hard labels in a training set. However, it is shown in the literature that the larger the gap between the teacher and the student networks, the more difficult is their training using knowledge distillation. To address this shortcoming, we propose an improved knowledge distillation method (called Annealing-KD) by feeding the rich information provided by the teacher's soft-targets incrementally and more efficiently. Our Annealing-KD technique is based on a gradual transition over annealed soft-targets generated by the teacher at different temperatures in an iterative process, and therefore, the student is trained to follow the annealed teacher output in a step-by-step manner. This paper includes theoretical and empirical evidence as well as practical experiments to support the effectiveness of our Annealing-KD method. We did a comprehensive set of experiments on different tasks such as image classification (CIFAR-10 and 100) and NLP language inference with BERT-based models on the GLUE benchmark and consistently got superior results.

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