Summary of Active Generation For Image Classification, by Tao Huang et al.
Active Generation for Image Classification
by Tao Huang, Jiaqi Liu, Shan You, Chang Xu
First submitted to arxiv on: 11 Mar 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 The paper proposes ActGen, an efficient method for enhancing image classification accuracy using deep generative models. Unlike existing methods that require generating a large number of images with limited improvements in accuracy, ActGen takes a training-aware approach to generate images similar to challenging or misclassified samples encountered by the current model. This approach incorporates generated images into the training set to augment model performance. ActGen introduces two techniques: attentive image guidance using real images as guides during the denoising process of a diffusion model, and gradient-based generation guidance that employs two losses to generate more challenging samples while preventing similar images from being generated. Experimental results on CIFAR and ImageNet datasets demonstrate better performance with reduced number of generated images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ActGen is a new method for improving image classification using deep generative models. It makes the process faster and more efficient by generating only the right kind of images to help the model learn. The approach is based on training-aware generation, which means it creates images that are similar to the tricky samples the current model gets wrong. This helps the model learn better without needing to generate a huge number of images. |
Keywords
» Artificial intelligence » Diffusion model » Image classification
