Summary of Discriminative and Consistent Representation Distillation, by Nikolaos Giakoumoglou et al.
Discriminative and Consistent Representation Distillation
by Nikolaos Giakoumoglou, Tania Stathaki
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Discriminative and Consistent Distillation (DCD) method employs a contrastive loss along with a consistency regularization to minimize the discrepancy between teacher and student model representations. This approach introduces learnable temperature and bias parameters that adapt during training to balance these complementary objectives, replacing fixed hyperparameters used in traditional contrastive learning approaches. The method achieves state-of-the-art performance on CIFAR-100 and ImageNet ILSVRC-2012, with the student model sometimes surpassing the teacher’s accuracy. Furthermore, DCD’s learned representations exhibit superior cross-dataset generalization when transferred to Tiny ImageNet and STL-10. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed method helps small models learn from large models by using a special kind of learning called contrastive learning. This approach makes sure the small model is similar to the big model in a way that doesn’t exist yet in knowledge distillation methods. The new method, called Discriminative and Consistent Distillation (DCD), uses adjustable parameters that change during training to balance two important goals. The result is a state-of-the-art performance on several image classification tasks. |
Keywords
» Artificial intelligence » Contrastive loss » Distillation » Generalization » Image classification » Knowledge distillation » Regularization » Student model » Temperature
