Summary of Reffakd: Resource-efficient Autoencoder-based Knowledge Distillation, by Divyang Doshi and Jung-eun Kim
ReffAKD: Resource-efficient Autoencoder-based Knowledge Distillation
by Divyang Doshi, Jung-Eun Kim
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This research proposes a novel method to enhance Knowledge Distillation efficiency without relying on computationally costly teacher models. The traditional approach uses soft labels from the teacher model to guide the training of a smaller “student” model, which can be resource-intensive. Instead, the authors employ a compact autoencoder to extract essential features and calculate similarity scores between classes, generating soft probability vectors that serve as guidance during student model training. The proposed method is tested on various datasets, including CIFAR-100, Tiny Imagenet, and Fashion MNIST, demonstrating superior resource efficiency compared to traditional methods while achieving similar or even better performance in terms of model accuracy. This approach can be easily added to logit-based knowledge distillation methods and has the potential to make knowledge distillation more accessible and cost-effective for practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study makes machine learning training faster and cheaper without using powerful computers. The traditional way to train smaller models uses a bigger model as a teacher, which can be slow and expensive. Instead, this research creates a new way to generate helpful clues from the data itself, eliminating the need for a big teacher model. The approach is tested on several datasets and shows it’s faster and more efficient while still producing good results. This method can be used with other training techniques and has the potential to make machine learning more accessible and affordable. |
Keywords
» Artificial intelligence » Autoencoder » Knowledge distillation » Machine learning » Probability » Student model » Teacher model
