Residual Knowledge Distillation

• URL: http://arxiv.org/abs/2002.09168v1
• Date: Fri, 21 Feb 2020 07:49:26 GMT
• Title: Residual Knowledge Distillation
• Authors: Mengya Gao, Yujun Shen, Quanquan Li, Chen Change Loy
• Abstract summary: This work proposes Residual Knowledge Distillation (RKD), which further distills the knowledge by introducing an assistant (A) In this way, S is trained to mimic the feature maps of T, and A aids this process by learning the residual error between them. Experiments show that our approach achieves appealing results on popular classification datasets, CIFAR-100 and ImageNet.
• Score: 96.18815134719975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge distillation (KD) is one of the most potent ways for model compression. The key idea is to transfer the knowledge from a deep teacher model (T) to a shallower student (S). However, existing methods suffer from performance degradation due to the substantial gap between the learning capacities of S and T. To remedy this problem, this work proposes Residual Knowledge Distillation (RKD), which further distills the knowledge by introducing an assistant (A). Specifically, S is trained to mimic the feature maps of T, and A aids this process by learning the residual error between them. In this way, S and A complement with each other to get better knowledge from T. Furthermore, we devise an effective method to derive S and A from a given model without increasing the total computational cost. Extensive experiments show that our approach achieves appealing results on popular classification datasets, CIFAR-100 and ImageNet, surpassing state-of-the-art methods.

Related papers

• Adaptive Explicit Knowledge Transfer for Knowledge Distillation [17.739979156009696]
  We show that the performance of logit-based knowledge distillation can be improved by effectively delivering the probability distribution for the non-target classes from the teacher model. We propose a new loss that enables the student to learn explicit knowledge along with implicit knowledge in an adaptive manner. Experimental results demonstrate that the proposed method, called adaptive explicit knowledge transfer (AEKT) method, achieves improved performance compared to the state-of-the-art KD methods.
  arXiv  Detail & Related papers  (2024-09-03T07:42:59Z)
• One Step Diffusion-based Super-Resolution with Time-Aware Distillation [60.262651082672235]
  Diffusion-based image super-resolution (SR) methods have shown promise in reconstructing high-resolution images with fine details from low-resolution counterparts. Recent techniques have been devised to enhance the sampling efficiency of diffusion-based SR models via knowledge distillation. We propose a time-aware diffusion distillation method, named TAD-SR, to accomplish effective and efficient image super-resolution.
  arXiv  Detail & Related papers  (2024-08-14T11:47:22Z)
• Teaching with Uncertainty: Unleashing the Potential of Knowledge Distillation in Object Detection [47.0507287491627]
  We propose a novel feature-based distillation paradigm with knowledge uncertainty for object detection. By leveraging the Monte Carlo dropout technique, we introduce knowledge uncertainty into the training process of the student model. Our method performs effectively during the KD process without requiring intricate structures or extensive computational resources.
  arXiv  Detail & Related papers  (2024-06-11T06:51:02Z)
• AdaKD: Dynamic Knowledge Distillation of ASR models using Adaptive Loss Weighting [5.818420448447701]
  We propose Adaptive Knowledge Distillation, a novel technique inspired by curriculum learning to adaptively weigh the losses at instance level. Our method follows a plug-and-play paradigm that can be applied on top of any task-specific and distillation objectives.
  arXiv  Detail & Related papers  (2024-05-11T15:06:24Z)
• Robustness-Reinforced Knowledge Distillation with Correlation Distance and Network Pruning [3.1423836318272773]
  Knowledge distillation (KD) improves the performance of efficient and lightweight models. Most existing KD techniques rely on Kullback-Leibler (KL) divergence. We propose a Robustness-Reinforced Knowledge Distillation (R2KD) that leverages correlation distance and network pruning.
  arXiv  Detail & Related papers  (2023-11-23T11:34:48Z)
• A Closer Look at Knowledge Distillation with Features, Logits, and Gradients [81.39206923719455]
  Knowledge distillation (KD) is a substantial strategy for transferring learned knowledge from one neural network model to another. This work provides a new perspective to motivate a set of knowledge distillation strategies by approximating the classical KL-divergence criteria with different knowledge sources. Our analysis indicates that logits are generally a more efficient knowledge source and suggests that having sufficient feature dimensions is crucial for the model design.
  arXiv  Detail & Related papers  (2022-03-18T21:26:55Z)
• Efficient training of lightweight neural networks using Online Self-Acquired Knowledge Distillation [51.66271681532262]
  Online Self-Acquired Knowledge Distillation (OSAKD) is proposed, aiming to improve the performance of any deep neural model in an online manner. We utilize k-nn non-parametric density estimation technique for estimating the unknown probability distributions of the data samples in the output feature space.
  arXiv  Detail & Related papers  (2021-08-26T14:01:04Z)
• Knowledge Distillation Thrives on Data Augmentation [65.58705111863814]
  Knowledge distillation (KD) is a general deep neural network training framework that uses a teacher model to guide a student model. Many works have explored the rationale for its success, however, its interplay with data augmentation (DA) has not been well recognized so far. In this paper, we are motivated by an interesting observation in classification: KD loss can benefit from extended training iterations while the cross-entropy loss does not. We show this disparity arises because of data augmentation: KD loss can tap into the extra information from different input views brought by DA.
  arXiv  Detail & Related papers  (2020-12-05T00:32:04Z)

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.