Summary of Diagen: Diverse Image Augmentation with Generative Models, by Tobias Lingenberg et al.
DIAGen: Diverse Image Augmentation with Generative Models
by Tobias Lingenberg, Markus Reuter, Gopika Sudhakaran, Dominik Gojny, Stefan Roth, Simone Schaub-Meyer
First submitted to arxiv on: 26 Aug 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 In this paper, researchers aim to improve the generalization power of computer vision models by developing a novel data augmentation method called DIAGen. The authors propose a technique that builds upon existing methods like DA-Fusion and applies Gaussian noise to object embeddings learned with Textual Inversion. This approach uses pre-trained diffusion models to generate diverse images with varied class-specific prompts, effectively diversifying semantic attributes. To mitigate poorly generated samples, the authors introduce a weighting mechanism. Experimental results across various datasets show that DIAGen not only enhances semantic diversity but also improves classifier performance, particularly with out-of-distribution samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DIAGen is a new way to make computer vision models better at recognizing objects. Usually, data augmentation techniques like rotating and flipping images are used to help the model learn more. But these methods don’t change how objects look in different situations or from different viewpoints. The researchers wanted to create a method that can generate images with varied semantic attributes, such as different breeds of dogs or weather conditions. They developed DIAGen by combining two existing techniques: one that adds noise to object embeddings and another that uses text-to-image generation. This new method shows promise in improving the performance of computer vision models. |
Keywords
» Artificial intelligence » Data augmentation » Generalization » Image generation
