Summary of Emphasizing Discriminative Features For Dataset Distillation in Complex Scenarios, by Kai Wang et al.
Emphasizing Discriminative Features for Dataset Distillation in Complex Scenarios
by Kai Wang, Zekai Li, Zhi-Qi Cheng, Samir Khaki, Ahmad Sajedi, Ramakrishna Vedantam, Konstantinos N Plataniotis, Alexander Hauptmann, Yang You
First submitted to arxiv on: 22 Oct 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 This paper proposes a novel dataset distillation method called EDF (emphasizes the discriminative features) that enhances key discriminative regions in synthetic images using Grad-CAM activation maps. The approach is inspired by the observation that high-activation areas typically occupy most of the image in simple datasets, but are much smaller in complex scenarios. Unlike previous methods, EDF uses Grad-CAM activation maps to enhance these areas. Additionally, the paper introduces the Complex Dataset Distillation (Comp-DD) benchmark, which includes 16 subsets from ImageNet-1K, and demonstrates that EDF outperforms state-of-the-art results in complex scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computer images that are more realistic. It uses special maps to find the most important parts of an image and makes them stand out. This helps the computer learn from simple images, like pictures of animals or cars, but also from harder ones, like real-life photos. The researchers made a special test set with lots of different images to see how well their method works. They found that it does much better than other methods on harder images. |
Keywords
» Artificial intelligence » Distillation
