Wasserstein Contrastive Representation Distillation

@article{Chen2021WassersteinCR,
  title={Wasserstein Contrastive Representation Distillation},
  author={Liqun Chen and Zhe Gan and Dong Wang and Jingjing Liu and Ricardo Henao and Lawrence Carin},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={16291-16300}
}
  • Liqun Chen, Zhe Gan, L. Carin
  • Published 15 December 2020
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
The primary goal of knowledge distillation (KD) is to encapsulate the information of a model learned from a teacher network into a student network, with the latter being more compact than the former. Existing work, e.g., using Kullback-Leibler divergence for distillation, may fail to capture important structural knowledge in the teacher network and often lacks the ability for feature generalization, particularly in situations when teacher and student are built to address different… 

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References

SHOWING 1-10 OF 53 REFERENCES

Contrastive Representation Distillation

TLDR
The resulting new objective outperforms knowledge distillation and other cutting-edge distillers on a variety of knowledge transfer tasks, including single model compression, ensemble distillation, and cross-modal transfer.

Similarity-Preserving Knowledge Distillation

TLDR
This paper proposes a new form of knowledge distillation loss that is inspired by the observation that semantically similar inputs tend to elicit similar activation patterns in a trained network.

Revisiting Knowledge Distillation via Label Smoothing Regularization

TLDR
It is argued that the success of KD is not fully due to the similarity information between categories from teachers, but also to the regularization of soft targets, which is equally or even more important.

Contrastive Distillation on Intermediate Representations for Language Model Compression

TLDR
CoDIR is proposed, a principled knowledge distillation framework where the student is trained to distill knowledge through intermediate layers of the teacher via a contrastive objective, and achieves superb performance on the GLUE benchmark, outperforming state-of-the-art compression methods.

Patient Knowledge Distillation for BERT Model Compression

TLDR
This work proposes a Patient Knowledge Distillation approach to compress an original large model (teacher) into an equally-effective lightweight shallow network (student), which translates into improved results on multiple NLP tasks with a significant gain in training efficiency, without sacrificing model accuracy.

Wasserstein Dependency Measure for Representation Learning

TLDR
It is empirically demonstrated that mutual information-based representation learning approaches do fail to learn complete representations on a number of designed and real-world tasks, and a practical approximation to this theoretically motivated solution, constructed using Lipschitz constraint techniques from the GAN literature, achieves substantially improved results on tasks where incomplete representations are a major challenge.

Knowledge Distillation by On-the-Fly Native Ensemble

TLDR
This work presents an On-the-fly Native Ensemble strategy for one-stage online distillation that improves the generalisation performance a variety of deep neural networks more significantly than alternative methods on four image classification dataset.

On Mutual Information Maximization for Representation Learning

TLDR
This paper argues, and provides empirical evidence, that the success of these methods cannot be attributed to the properties of MI alone, and that they strongly depend on the inductive bias in both the choice of feature extractor architectures and the parametrization of the employed MI estimators.

FitNets: Hints for Thin Deep Nets

TLDR
This paper extends the idea of a student network that could imitate the soft output of a larger teacher network or ensemble of networks, using not only the outputs but also the intermediate representations learned by the teacher as hints to improve the training process and final performance of the student.

A Theoretical Analysis of Contrastive Unsupervised Representation Learning

TLDR
This framework allows us to show provable guarantees on the performance of the learned representations on the average classification task that is comprised of a subset of the same set of latent classes and shows that learned representations can reduce (labeled) sample complexity on downstream tasks.
...