Summary of Effective Data Augmentation with Diffusion Models, by Brandon Trabucco et al.
Effective Data Augmentation With Diffusion Models
by Brandon Trabucco, Kyle Doherty, Max Gurinas, Ruslan Salakhutdinov
First submitted to arxiv on: 7 Feb 2023
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 Data augmentation is a crucial tool in deep learning, enabling advancements in classification, generative models, and representation learning. While standard augmentations like rotations and flips create new images from existing ones, they lack diversity along key semantic axes. Our research addresses this limitation by introducing image-to-image transformations parameterized by pre-trained text-to-image diffusion models. These models can edit images to change their semantics using few labelled examples. We evaluate our approach on few-shot image classification tasks and a real-world weed recognition task, observing improved accuracy in tested domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data augmentation is important in deep learning because it helps create more training data for machines to learn from. Currently, this process doesn’t do enough to make the new images look different from each other. Our solution uses pre-trained models that can change the meaning of an image by adding or removing certain things. We test our approach on recognizing plants and find that it works better than usual. |
Keywords
* Artificial intelligence * Classification * Data augmentation * Deep learning * Few shot * Image classification * Representation learning * Semantics
