Summary of Tiny Models From Tiny Data: Textual and Null-text Inversion For Few-shot Distillation, by Erik Landolsi et al.
Tiny models from tiny data: Textual and null-text inversion for few-shot distillation
by Erik Landolsi, Fredrik Kahl
First submitted to arxiv on: 5 Jun 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 This paper explores ways to improve few-shot image classification by leveraging knowledge distillation. Foundation models excel at this task but are computationally expensive due to their large size. The authors propose a method that transfers the capabilities of high-performing yet slow models to tiny, efficient ones using synthetic data. They aim to overcome the limitation of requiring unlabeled data for traditional distillation methods in few-shot settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at recognizing pictures even when they only have a few examples to learn from. Big AI models are really good at this task but take a long time to process information. Scientists want to shrink these models while keeping their abilities, so they’re trying to copy the knowledge from big models into smaller ones using fake data. |
Keywords
» Artificial intelligence » Distillation » Few shot » Image classification » Knowledge distillation » Synthetic data
