This paper aims to provide a selective survey about knowledge distillation(KD) framework for researchers and practitioners to take advantage of it for developing new optimized models in the deep neural network field. To this end, we give a brief overview of knowledge distillation and some related works including learning using privileged information(LUPI) and generalized distillation(GD). Even though knowledge distillation based on the teacher-student architecture was initially devised as a model compression technique, it has found versatile applications over various frameworks.
In this paper, we review the characteristics of knowledge distillation from the hypothesis that the three important ingredients of knowledge distillation are distilled knowledge and loss,teacher-student paradigm, and the distillation process. In addition, we survey the versatility of the knowledge distillation by studying its direct applications and its usage in combination with other deep learning paradigms. Finally we present some future works in knowledge distillation including explainable knowledge distillation where the analytical analysis of the performance gain is studied and the self-supervised learning which is a hot research topic in deep learning community.
Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks
Faculty of Information Technolody, Monash University, Australia（墨尔本大学信息科技学院，澳大利亚）；School of Software, Northeastern University, China（东北大学软件学院，中国）；Department of Medicine, National University of Singapore, Singapore（新加坡国立大学医学部，新加坡）
In this paper, we propose a Siamese sleep transformer (SST) that effectively extracts features from single-channel raw electroencephalogram signals for robust sleep stage scoring. Despite the significant advances in sleep stage scoring in the last few years, most of them mainly focused on the increment of model performance. However, other problems still exist: the bias of labels in datasets and the instability of model performance by repetitive training. To alleviate these problems, we propose the SST, a novel sleep stage scoring model with a selective batch sampling strategy and self-knowledg…
Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation
Department of Computer Science and Engineering, Seoul National University of Science and Technology（首尔科学技术大学计算机科学与工程系）；Geometric Media Lab, School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学几何媒体实验室）；School for Engineering of Matter, Transport and Energy, Arizona State University（亚利桑那州立大学工程与能量科学学院）
Hasso Plattner Institute（哈索·普拉特纳研究所）；University of Oxford（牛津大学）；Zenith Ai（Zenith人工智能公司）；Renmin University of China（中国人民大学）；Queen’s University Belfast（贝尔法斯特女王大学）
Mutual knowledge distillation (MKD) improves a model by distilling knowledge from another model. However, \textit{not all knowledge is certain and correct}, especially under adverse conditions. For example, label noise usually leads to less reliable models due to undesired memorization \cite{zhang2017understanding,arpit2017closer}. Wrong knowledge misleads the learning rather than helps. This problem can be handled by two aspects: (i) improving the reliability of a model where the knowledge is from (i.e., knowledge source's reliability); (ii) selecting reliable knowledge for distillation. In t…
Graduate School of Convergence Science and Technology（融合科学与技术研究生院）；Seoul National University（首尔国立大学）
Knowledge Distillation (KD) aims to transfer knowledge in a teacher-student framework, by providing the predictions of the teacher network to the student network in the training stage to help the student network generalize better. It can use either a teacher with high capacity or {an} ensemble of multiple teachers. However, the latter is not convenient when one wants to use feature-map-based distillation methods. For a solution, this paper proposes a versatile and powerful training algorithm named FEature-level Ensemble for knowledge Distillation (FEED), which aims to transfer the ensemble kno…
We propose a method to infer a dense depth map from a single image, its calibration, and the associated sparse point cloud. In order to leverage existing models (teachers) that produce putative depth maps, we propose an adaptive knowledge distillation approach that yields a positive congruent training process, wherein a student model avoids learning the error modes of the teachers. In the absence of ground truth for model selection and training, our method, termed Monitored Distillation, allows a student to exploit a blind ensemble of teachers by selectively learning from predictions that best…
Although deep Reinforcement Learning (RL) has proven successful in a wide range of tasks, one challenge it faces is interpretability when applied to real-world problems. Saliency maps are frequently used to provide interpretability for deep neural networks. However, in the RL domain, existing saliency map approaches are either computationally expensive and thus cannot satisfy the real-time requirement of real-world scenarios or cannot produce interpretable saliency maps for RL policies. In this work, we propose an approach of Distillation with selective Input Gradient Regularization (DIGR) whi…
Uncertainty-Aware Dual-Student Knowledge Distillation for Efficient Image Classification
Department of Electrical Engineering Indian Institute of Technology Bombay 21D070002（电子工程系 印度理工学院班加罗尔 21D070002）；Department of Electrical Engineering Indian Institute of Technology Bombay 210010010（电子工程系 印度理工学院班加罗尔 210010010）；Department of Electrical Engineering Indian Institute of Technology Bombay 210020023（电子工程系 印度理工学院班加罗尔 210020023）
Rethinking Decoupled Knowledge Distillation: A Predictive Distribution Perspective
Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University（数据科学与人工智能系，香港理工大学）；Department of Computing, The Hong Kong Polytechnic University（计算系，香港理工大学）；The Hong Kong Polytechnic University Shenzhen Research Institute（香港理工大学深圳研究院）
STEPS: Selective On-Policy Self-Distillation for Reasoning
State Key Laboratory of Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；AMAP, Alibaba Group（阿里集团AMAP）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Tsinghua University（清华大学）
Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Data
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences and the University of Chinese Academy of Sciences（北京移动计算与 pervasive 设备重点实验室，计算技术研究所，中国科学院和中国科学院大学）；Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences（北京移动计算与 pervasive 设备重点实验室，计算技术研究所，中国科学院）；Shandong Academy of Intelligent Computing Technology（山东省智能计算技术研究院）；Frontier Sciences and Education, Chinese Academy of Sciences（前沿科学与教育，中国科学院）
The outpouring of various pre-trained models empowers knowledge distillation by providing abundant teacher resources, but there lacks a developed mechanism to utilize these teachers adequately. With a massive model repository composed of teachers pre-trained on diverse tasks, we must surmount two obstacles when using knowledge distillation to learn a new task. First, given a fixed computing budget, it is not affordable to try each teacher and train the student repeatedly, making it necessary to seek out the most contributive teacher precisely and efficiently. Second, semantic gaps exist betwee…
Not All Disagreement Is Learnable: Token Teachability in On-Policy Distillation
The Hong Kong Polytechnic University, PolyU（香港理工大学）；Hong Kong Polytechnic University（香港理工大学）；Daya Bay Technology and Innovation Research Institute（大亚湾技术与创新研究院）
SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Chongqing University（重庆大学）；Peng Cheng Laboratory（鹏城实验室）；Wuhan AI Research（武汉人工智能研究院）
