Teaching with Uncertainty: Unleashing the Potential of Knowledge Distillation in Object Detection

• URL: http://arxiv.org/abs/2406.06999v1
• Date: Tue, 11 Jun 2024 06:51:02 GMT
• Title: Teaching with Uncertainty: Unleashing the Potential of Knowledge Distillation in Object Detection
• Authors: Junfei Yi, Jianxu Mao, Tengfei Liu, Mingjie Li, Hanyu Gu, Hui Zhang, Xiaojun Chang, Yaonan Wang,
• Abstract summary: 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.
• Score: 47.0507287491627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge distillation (KD) is a widely adopted and effective method for compressing models in object detection tasks. Particularly, feature-based distillation methods have shown remarkable performance. Existing approaches often ignore the uncertainty in the teacher model's knowledge, which stems from data noise and imperfect training. This limits the student model's ability to learn latent knowledge, as it may overly rely on the teacher's imperfect guidance. In this paper, we propose a novel feature-based distillation paradigm with knowledge uncertainty for object detection, termed "Uncertainty Estimation-Discriminative Knowledge Extraction-Knowledge Transfer (UET)", which can seamlessly integrate with existing distillation methods. By leveraging the Monte Carlo dropout technique, we introduce knowledge uncertainty into the training process of the student model, facilitating deeper exploration of latent knowledge. Our method performs effectively during the KD process without requiring intricate structures or extensive computational resources. Extensive experiments validate the effectiveness of our proposed approach across various distillation strategies, detectors, and backbone architectures. Specifically, following our proposed paradigm, the existing FGD method achieves state-of-the-art (SoTA) performance, with ResNet50-based GFL achieving 44.1% mAP on the COCO dataset, surpassing the baselines by 3.9%.

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)
• Learning Lightweight Object Detectors via Multi-Teacher Progressive Distillation [56.053397775016755]
  We propose a sequential approach to knowledge distillation that progressively transfers the knowledge of a set of teacher detectors to a given lightweight student. To the best of our knowledge, we are the first to successfully distill knowledge from Transformer-based teacher detectors to convolution-based students.
  arXiv  Detail & Related papers  (2023-08-17T17:17:08Z)
• Knowledge Distillation via Token-level Relationship Graph [12.356770685214498]
  We propose a novel method called Knowledge Distillation with Token-level Relationship Graph (TRG) By employing TRG, the student model can effectively emulate higher-level semantic information from the teacher model. We conduct experiments to evaluate the effectiveness of the proposed method against several state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-06-20T08:16:37Z)
• Gradient-Guided Knowledge Distillation for Object Detectors [3.236217153362305]
  We propose a novel approach for knowledge distillation in object detection, named Gradient-guided Knowledge Distillation (GKD) Our GKD uses gradient information to identify and assign more weights to features that significantly impact the detection loss, allowing the student to learn the most relevant features from the teacher. Experiments on the KITTI and COCO-Traffic datasets demonstrate our method's efficacy in knowledge distillation for object detection.
  arXiv  Detail & Related papers  (2023-03-07T21:09:09Z)
• Exploring Inconsistent Knowledge Distillation for Object Detection with Data Augmentation [66.25738680429463]
  Knowledge Distillation (KD) for object detection aims to train a compact detector by transferring knowledge from a teacher model. We propose inconsistent knowledge distillation (IKD) which aims to distill knowledge inherent in the teacher model's counter-intuitive perceptions. Our method outperforms state-of-the-art KD baselines on one-stage, two-stage and anchor-free object detectors.
  arXiv  Detail & Related papers  (2022-09-20T16:36:28Z)
• 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)
• 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)
• Residual Knowledge Distillation [96.18815134719975]
  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.
  arXiv  Detail & Related papers  (2020-02-21T07:49:26Z)

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.