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FerKD: Surgical Label Adaptation for Efficient Distillation (2312.17473v1)

Published 29 Dec 2023 in cs.CV, cs.AI, cs.LG, and eess.IV

Abstract: We present FerKD, a novel efficient knowledge distillation framework that incorporates partial soft-hard label adaptation coupled with a region-calibration mechanism. Our approach stems from the observation and intuition that standard data augmentations, such as RandomResizedCrop, tend to transform inputs into diverse conditions: easy positives, hard positives, or hard negatives. In traditional distillation frameworks, these transformed samples are utilized equally through their predictive probabilities derived from pretrained teacher models. However, merely relying on prediction values from a pretrained teacher, a common practice in prior studies, neglects the reliability of these soft label predictions. To address this, we propose a new scheme that calibrates the less-confident regions to be the context using softened hard groundtruth labels. Our approach involves the processes of hard regions mining + calibration. We demonstrate empirically that this method can dramatically improve the convergence speed and final accuracy. Additionally, we find that a consistent mixing strategy can stabilize the distributions of soft supervision, taking advantage of the soft labels. As a result, we introduce a stabilized SelfMix augmentation that weakens the variation of the mixed images and corresponding soft labels through mixing similar regions within the same image. FerKD is an intuitive and well-designed learning system that eliminates several heuristics and hyperparameters in former FKD solution. More importantly, it achieves remarkable improvement on ImageNet-1K and downstream tasks. For instance, FerKD achieves 81.2% on ImageNet-1K with ResNet-50, outperforming FKD and FunMatch by remarkable margins. Leveraging better pre-trained weights and larger architectures, our finetuned ViT-G14 even achieves 89.9%. Our code is available at https://github.com/szq0214/FKD/tree/main/FerKD.

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References (60)
  1. Better fine-tuning by reducing representational collapse. arXiv preprint arXiv:2008.03156, 2020.
  2. Beit: Bert pre-training of image transformers. In ICLR, 2022.
  3. Curriculum learning. In ICML, 2009.
  4. Are we done with imagenet? arXiv preprint arXiv:2006.07159, 2020.
  5. Knowledge distillation: A good teacher is patient and consistent. In CVPR, 2022.
  6. Learning efficient object detection models with knowledge distillation. NeurIPS, 30, 2017.
  7. Feature-map-level online adversarial knowledge distillation. In ICML, 2020.
  8. General instance distillation for object detection. In CVPR, 2021.
  9. Imagenet: A large-scale hierarchical image database. In CVPR, 2009.
  10. Eva: Exploring the limits of masked visual representation learning at scale. arXiv preprint arXiv:2211.07636, 2022.
  11. Distilling object detectors via decoupled features. In CVPR, 2021.
  12. Mask r-cnn. In ICCV, 2017.
  13. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015.
  14. The inaturalist species classification and detection dataset. In CVPR, 2018.
  15. Point-to-voxel knowledge distillation for lidar semantic segmentation. In CVPR, 2022.
  16. Structural and statistical texture knowledge distillation for semantic segmentation. In CVPR, 2022.
  17. What would elsa do? freezing layers during transformer fine-tuning. arXiv preprint arXiv:1911.03090, 2019.
  18. Lujun Li. Self-regulated feature learning via teacher-free feature distillation. In European Conference on Computer Vision, pages 347–363. Springer, 2022.
  19. Feature pyramid networks for object detection. In CVPR, 2017.
  20. Focal loss for dense object detection. In ICCV, 2017.
  21. Microsoft coco: Common objects in context. In ECCV, 2014.
  22. Structured knowledge distillation for semantic segmentation. In CVPR, 2019.
  23. Online batch selection for faster training of neural networks. arXiv preprint arXiv:1511.06343, 2015.
  24. Joel Michael. Where’s the evidence that active learning works? Advances in physiology education, 2006.
  25. When does label smoothing help? In NeurIPS, 2019.
  26. Distillation as a defense to adversarial perturbations against deep neural networks. In IEEE symposium on security and privacy (SP), 2016.
  27. Relational knowledge distillation. In CVPR, 2019.
  28. Michael Prince. Does active learning work? a review of the research. Journal of engineering education, 93(3):223–231, 2004.
  29. Do imagenet classifiers generalize to imagenet? In ICML, 2019.
  30. Autolr: Layer-wise pruning and auto-tuning of learning rates in fine-tuning of deep networks. In AAAI, 2021.
  31. Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550, 2014.
  32. Burr Settles. Active learning literature survey. 2009.
  33. Meal: Multi-model ensemble via adversarial learning. In AAAI, 2019.
  34. Partial is better than all: Revisiting fine-tuning strategy for few-shot learning. In AAAI, 2021.
  35. Is label smoothing truly incompatible with knowledge distillation: An empirical study. In ICLR, 2021.
  36. Meal v2: Boosting vanilla resnet-50 to 80%+ top-1 accuracy on imagenet without tricks. arXiv preprint arXiv:2009.08453, 2020.
  37. A fast knowledge distillation framework for visual recognition. In ECCV, 2022.
  38. Training region-based object detectors with online hard example mining. In CVPR, 2016.
  39. Revisiting weakly supervised pre-training of visual perception models. In CVPR, 2022.
  40. Curriculum learning: A survey. International Journal of Computer Vision, 130(6):1526–1565, 2022.
  41. Does knowledge distillation really work? arXiv preprint arXiv:2106.05945, 2021.
  42. How to train your vit? data, augmentation, and regularization in vision transformers. arXiv preprint arXiv:2106.10270, 2021.
  43. Rethinking the inception architecture for computer vision. In CVPR, 2016.
  44. PyTorch TorchVision IMAGENET1K_V2. https://pytorch.org/vision/stable/models/generated/torchvision.models.resnet50.html#torchvision.models.ResNet50_Weights. 2022.
  45. Training data-efficient image transformers & distillation through attention. In ICML, 2021.
  46. Distilling object detectors with fine-grained feature imitation. In CVPR, 2019.
  47. Dataset distillation. arXiv preprint arXiv:1811.10959, 2018.
  48. A survey on curriculum learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(9):4555–4576, 2021.
  49. Resnet strikes back: An improved training procedure in timm. arXiv preprint arXiv:2110.00476, 2021.
  50. Tinyvit: Fast pretraining distillation for small vision transformers. In ECCV, 2022.
  51. Norm: Knowledge distillation via n-to-one representation matching. In ICLR, 2023.
  52. Self-training with noisy student improves imagenet classification. In CVPR, 2020.
  53. Focal and global knowledge distillation for detectors. In CVPR, 2022.
  54. A gift from knowledge distillation: Fast optimization, network minimization and transfer learning. In CVPR, 2017.
  55. How transferable are features in deep neural networks? NeurIPS, 27, 2014.
  56. Cutmix: Regularization strategy to train strong classifiers with localizable features. In ICCV, 2019.
  57. Re-labeling imagenet: from single to multi-labels, from global to localized labels. In CVPR, 2021.
  58. mixup: Beyond empirical risk minimization. In ICLR, 2018.
  59. Be your own teacher: Improve the performance of convolutional neural networks via self distillation. In ICCV, 2019.
  60. Learning deep features for scene recognition using places database. NeurIPS, 2014.
Citations (1)
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Summary

  • The paper presents FerKD, an adaptive distillation framework that dynamically calibrates low-confidence regions with hard labels.
  • It employs a novel region-based strategy during augmentation to adjust soft labels, enhancing convergence speed and model accuracy.
  • Empirical results show FerKD outperforming previous methods, with accuracies of 81.2% on ResNet-50 and up to 89.9% on ViT-G14.

Insightful Overview of "FerKD: Surgical Label Adaptation for Efficient Distillation"

In the academic paper titled "FerKD: Surgical Label Adaptation for Efficient Distillation," the authors present an advanced framework for knowledge distillation that integrates partial soft-hard label adaptation with a region-calibration mechanism. The research work aims to address the limitations of traditional knowledge distillation methodologies, particularly the computational inefficiencies and reliability issues related to soft label predictions derived from pretrained teacher models.

Methodological Insights

The FerKD approach builds on the observation that standard data augmentations, like RandomResizedCrop, generate samples that possess varying degrees of difficulty: easy positives, hard positives, or hard negatives. Conventional knowledge distillation frameworks treat these diverse samples equivalently, relying heavily on predictive probabilities from teacher models, potentially leading to inaccuracies. FerKD innovatively addresses this by selectively calibrating less-confident regions using softened hard ground-truth labels, thus refining the distillation process through a procedure termed as "hard regions mining and calibration."

This process involves a careful categorization of regions based on their associated predictive probabilities, which allows the framework to recalibrate the soft labels dynamically, effectively enhancing convergence rates and final model accuracy. This nuanced treatment of regions categorized as extremely hard negatives, moderate hard contexts, hard positives, and easy positives facilitates improved learning by adjusting the confidence levels of predictions in challenging areas. The authors introduce a stabilized augmentation strategy named SelfMix that moderates the variations in mixed images, tailoring the augmentation to reinforce the quality of soft labels.

Empirical Results and Implementation

The paper provides empirical evidence that FerKD achieves substantial improvements in terms of both convergence speed and accuracy on large-scale datasets such as ImageNet-1K, outperforming previous methods like FKD and FunMatch. Notably, with a ResNet-50 network, FerKD attains an accuracy of 81.2%, whereas larger architectures finetuned with FerKD, such as ViT-G14, reach up to 89.9%. These results underscore the efficacy of the surgical label adaptation and the importance of strategic soft label usage.

In addition, the paper illustrates the applicability of FerKD beyond image classification, suggesting its adaptability for tasks such as object detection and semantic segmentation. The strategic utilization of soft-hard label calibration and the operational simplicity of the region-calibration mechanism provides a robust, efficient solution that minimizes computational overhead while maximizing model performance.

Theoretical and Practical Implications

The findings presented in this paper offer meaningful theoretical implications by challenging the established practice of uniform sample treatment in knowledge distillation and proposing a strategic, region-centric alternative. Practically, FerKD implies significant advancements in the development of efficient machine learning models, particularly in scenarios with resource constraints and the necessity for rapid model deployment.

Looking forward, this research lays the foundation for further exploration into adaptive label usage and the potential for new augmentation strategies tailored to the unique challenges of dynamic soft label contexts. Future work could explore optimizing the balance between label calibration intricacies and computational resources, potentially further enhancing the scalability and efficiency of AI models.

In conclusion, FerKD significantly contributes to the field of efficient knowledge distillation by pioneering an adaptive, calibration-based approach that addresses longstanding limitations in teacher-student model frameworks, paving the way for more adept application of machine learning in complex domains.

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Authors (1)

  1. Zhiqiang Shen
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