Summary of Data-to-model Distillation: Data-efficient Learning Framework, by Ahmad Sajedi et al.
Data-to-Model Distillation: Data-Efficient Learning Framework
by Ahmad Sajedi, Samir Khaki, Lucy Z. Liu, Ehsan Amjadian, Yuri A. Lawryshyn, Konstantinos N. Plataniotis
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Data-to-Model Distillation (D2M) framework distills knowledge from a large-scale real dataset into the learnable parameters of a pre-trained generative model, enabling the production of informative training images for various distillation ratios and deep architectures. This is achieved by aligning rich representations extracted from real and generated images. The D2M method outperforms existing approaches in terms of computational efficiency, scalability to complex high-resolution datasets, and generalizability to deep architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dataset distillation aims to shrink large-scale data into smaller yet informative synthetic data, making it easier for models to learn from it. Existing methods struggle with speed, size, and working well with different types of models. The new D2M method fixes these issues by transferring knowledge from real images to a special type of model that can generate new images. This lets the model create training images for any desired level of detail or type of model. Tests on many datasets show that D2M works better than before and is useful for practical applications. |
Keywords
» Artificial intelligence » Distillation » Generative model » Synthetic data
