CrossKD: Cross-Head Knowledge Distillation for Object Detection

• URL: http://arxiv.org/abs/2306.11369v2
• Date: Mon, 15 Apr 2024 09:33:21 GMT
• Title: CrossKD: Cross-Head Knowledge Distillation for Object Detection
• Authors: Jiabao Wang, Yuming Chen, Zhaohui Zheng, Xiang Li, Ming-Ming Cheng, Qibin Hou,
• Abstract summary: Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. We present a prediction mimicking distillation scheme, called CrossKD, which delivers the intermediate features of the student's detection head to the teacher's detection head. Our CrossKD boosts the average precision of GFL ResNet-50 with 1x training schedule from 40.2 to 43.7, outperforming all existing KD methods.
• Score: 69.16346256926842
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge Distillation (KD) has been validated as an effective model compression technique for learning compact object detectors. Existing state-of-the-art KD methods for object detection are mostly based on feature imitation. In this paper, we present a general and effective prediction mimicking distillation scheme, called CrossKD, which delivers the intermediate features of the student's detection head to the teacher's detection head. The resulting cross-head predictions are then forced to mimic the teacher's predictions. This manner relieves the student's head from receiving contradictory supervision signals from the annotations and the teacher's predictions, greatly improving the student's detection performance. Moreover, as mimicking the teacher's predictions is the target of KD, CrossKD offers more task-oriented information in contrast with feature imitation. On MS COCO, with only prediction mimicking losses applied, our CrossKD boosts the average precision of GFL ResNet-50 with 1x training schedule from 40.2 to 43.7, outperforming all existing KD methods. In addition, our method also works well when distilling detectors with heterogeneous backbones. Code is available at https://github.com/jbwang1997/CrossKD.

Related papers

• Relative Difficulty Distillation for Semantic Segmentation [54.76143187709987]
  We propose a pixel-level KD paradigm for semantic segmentation named Relative Difficulty Distillation (RDD) RDD allows the teacher network to provide effective guidance on learning focus without additional optimization goals. Our research showcases that RDD can integrate with existing KD methods to improve their upper performance bound.
  arXiv  Detail & Related papers  (2024-07-04T08:08:25Z)
• Grouped Knowledge Distillation for Deep Face Recognition [53.57402723008569]
  The light-weight student network has difficulty fitting the target logits due to its low model capacity. We propose a Grouped Knowledge Distillation (GKD) that retains the Primary-KD and Binary-KD but omits Secondary-KD in the ultimate KD loss calculation.
  arXiv  Detail & Related papers  (2023-04-10T09:04:38Z)
• 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)
• Prediction-Guided Distillation for Dense Object Detection [7.5320132424481505]
  We show that only a very small fraction of features within a ground-truth bounding box are responsible for a teacher's high detection performance. We propose Prediction-Guided Distillation (PGD), which focuses distillation on these key predictive regions of the teacher. Our proposed approach outperforms current state-of-the-art KD baselines on a variety of advanced one-stage detection architectures.
  arXiv  Detail & Related papers  (2022-03-10T16:46:05Z)
• Knowledge Distillation for Object Detection via Rank Mimicking and Prediction-guided Feature Imitation [34.441349114336994]
  We propose Rank Mimicking (RM) and Prediction-guided Feature Imitation (PFI) for distilling one-stage detectors. RM takes the rank of candidate boxes from teachers as a new form of knowledge to distill. PFI attempts to correlate feature differences with prediction differences, making feature imitation directly help to improve the student's accuracy.
  arXiv  Detail & Related papers  (2021-12-09T11:19:15Z)
• How and When Adversarial Robustness Transfers in Knowledge Distillation? [137.11016173468457]
  This paper studies how and when the adversarial robustness can be transferred from a teacher model to a student model in Knowledge distillation (KD) We show that standard KD training fails to preserve adversarial robustness, and we propose KD with input gradient alignment (KDIGA) for remedy. Under certain assumptions, we prove that the student model using our proposed KDIGA can achieve at least the same certified robustness as the teacher model.
  arXiv  Detail & Related papers  (2021-10-22T21:30:53Z)
• Distilling and Transferring Knowledge via cGAN-generated Samples for Image Classification and Regression [17.12028267150745]
  We propose a unified KD framework based on conditional generative adversarial networks (cGANs) cGAN-KD distills and transfers knowledge from a teacher model to a student model via cGAN-generated samples. Experiments on CIFAR-10 and Tiny-ImageNet show we can incorporate KD methods into the cGAN-KD framework to reach a new state of the art.
  arXiv  Detail & Related papers  (2021-04-07T14:52:49Z)
• How Robust are Randomized Smoothing based Defenses to Data Poisoning? [66.80663779176979]
  We present a previously unrecognized threat to robust machine learning models that highlights the importance of training-data quality. We propose a novel bilevel optimization-based data poisoning attack that degrades the robustness guarantees of certifiably robust classifiers. Our attack is effective even when the victim trains the models from scratch using state-of-the-art robust training methods.
  arXiv  Detail & Related papers  (2020-12-02T15:30:21Z)

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.