Instance Temperature Knowledge Distillation

• URL: http://arxiv.org/abs/2407.00115v3
• Date: Sun, 7 Jul 2024 15:25:05 GMT
• Title: Instance Temperature Knowledge Distillation
• Authors: Zhengbo Zhang, Yuxi Zhou, Jia Gong, Jun Liu, Zhigang Tu,
• Abstract summary: Existing methods dynamically adjust the temperature to enable the student network to adapt to varying learning difficulties. We formulate the adjustment of temperature as a sequential decision-making task and propose a method based on reinforcement learning. Our framework can serve as a plug-and-play technique to be inserted into various KD methods easily.
• Score: 15.095465128404161
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge distillation (KD) enhances the performance of a student network by allowing it to learn the knowledge transferred from a teacher network incrementally. Existing methods dynamically adjust the temperature to enable the student network to adapt to the varying learning difficulties at different learning stages of KD. KD is a continuous process, but when adjusting the temperature, these methods consider only the immediate benefits of the operation in the current learning phase and fail to take into account its future returns. To address this issue, we formulate the adjustment of temperature as a sequential decision-making task and propose a method based on reinforcement learning, termed RLKD. Importantly, we design a novel state representation to enable the agent to make more informed action (i.e. instance temperature adjustment). To handle the problem of delayed rewards in our method due to the KD setting, we explore an instance reward calibration approach. In addition,we devise an efficient exploration strategy that enables the agent to learn valuable instance temperature adjustment policy more efficiently. Our framework can serve as a plug-and-play technique to be inserted into various KD methods easily, and we validate its effectiveness on both image classification and object detection tasks. Our project is at https://www.zayx.me/ITKD.github.io/.

Related papers

• Adaptive Rentention & Correction for Continual Learning [114.5656325514408]
  A common problem in continual learning is the classification layer's bias towards the most recent task. We name our approach Adaptive Retention & Correction (ARC) ARC achieves an average performance increase of 2.7% and 2.6% on the CIFAR-100 and Imagenet-R datasets.
  arXiv  Detail & Related papers  (2024-05-23T08:43:09Z)
• Dynamic Temperature Knowledge Distillation [9.6046915661065]
  Temperature plays a pivotal role in moderating label softness in the realm of knowledge distillation (KD) Traditional approaches often employ a static temperature throughout the KD process. We propose Dynamic Temperature Knowledge Distillation (DTKD) which introduces a dynamic, cooperative temperature control for both teacher and student models simultaneously.
  arXiv  Detail & Related papers  (2024-04-19T08:40:52Z)
• Temperature Balancing, Layer-wise Weight Analysis, and Neural Network Training [58.20089993899729]
  This paper proposes TempBalance, a straightforward yet effective layerwise learning rate method. We show that TempBalance significantly outperforms ordinary SGD and carefully-tuned spectral norm regularization. We also show that TempBalance outperforms a number of state-of-the-art metrics and schedulers.
  arXiv  Detail & Related papers  (2023-12-01T05:38:17Z)
• Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning [14.379472108242235]
  We investigate exemplar-free class incremental learning (CIL) with knowledge distillation (KD) as a regularization strategy. KD-based methods are successfully used in CIL, but they often struggle to regularize the model without access to exemplars of the training data from previous tasks. Inspired by recent test-time adaptation methods, we introduce Teacher Adaptation (TA), a method that concurrently updates the teacher and the main models during incremental training.
  arXiv  Detail & Related papers  (2023-08-18T13:22:59Z)
• Knowledge Diffusion for Distillation [53.908314960324915]
  The representation gap between teacher and student is an emerging topic in knowledge distillation (KD) We state that the essence of these methods is to discard the noisy information and distill the valuable information in the feature. We propose a novel KD method dubbed DiffKD, to explicitly denoise and match features using diffusion models.
  arXiv  Detail & Related papers  (2023-05-25T04:49:34Z)
• Curriculum Temperature for Knowledge Distillation [30.94721463833605]
  We propose a curriculum-based technique, termed Curriculum Temperature for Knowledge Distillation (CTKD) CTKD controls the task difficulty level during the student's learning career through a dynamic and learnable temperature. As an easy-to-use plug-in technique, CTKD can be seamlessly integrated into existing knowledge distillation frameworks.
  arXiv  Detail & Related papers  (2022-11-29T14:10:35Z)
• Parameter-Efficient and Student-Friendly Knowledge Distillation [83.56365548607863]
  We present a parameter-efficient and student-friendly knowledge distillation method, namely PESF-KD, to achieve efficient and sufficient knowledge transfer. Experiments on a variety of benchmarks show that PESF-KD can significantly reduce the training cost while obtaining competitive results compared to advanced online distillation methods.
  arXiv  Detail & Related papers  (2022-05-28T16:11:49Z)
• Undistillable: Making A Nasty Teacher That CANNOT teach students [84.6111281091602]
  This paper introduces and investigates a concept called Nasty Teacher: a specially trained teacher network that yields nearly the same performance as a normal one. We propose a simple yet effective algorithm to build the nasty teacher, called self-undermining knowledge distillation.
  arXiv  Detail & Related papers  (2021-05-16T08:41:30Z)
• Annealing Knowledge Distillation [5.396407687999048]
  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.
  arXiv  Detail & Related papers  (2021-04-14T23:45:03Z)
• Heterogeneous Knowledge Distillation using Information Flow Modeling [82.83891707250926]
  We propose a novel KD method that works by modeling the information flow through the various layers of the teacher model. The proposed method is capable of overcoming the aforementioned limitations by using an appropriate supervision scheme during the different phases of the training process.
  arXiv  Detail & Related papers  (2020-05-02T06:56:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.