Summary of Hybrid Diffusion Models: Combining Supervised and Generative Pretraining For Label-efficient Fine-tuning Of Segmentation Models, by Bruno Sauvalle et al.
Hybrid diffusion models: combining supervised and generative pretraining for label-efficient fine-tuning of segmentation models
by Bruno Sauvalle, Mathieu Salzmann
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 novel approaches for label-efficient fine-tuning of segmentation models, particularly in scenarios where a large labeled dataset is available in one domain and limited samples are available in another. The authors introduce two distinct methods: supervised pretraining and self-supervised pretraining with a generic pretext task. They then propose fusing these approaches by introducing a new pretext task that combines image denoising and mask prediction. This allows for the generation of high-quality representations that can be used to train models on the second domain in a label-efficient manner. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make AI models better at recognizing things like objects or shapes in images, even when there are only a few examples to learn from. It tries two different ways: one where the model is trained using lots of labeled data and then fine-tuned with limited new data, and another where the model is trained without labels using a special task that helps it learn to recognize things on its own. The authors then combine these two approaches by having the model do something called image denoising (removing noise from images) at the same time as trying to predict what’s in an image. They show that this combination leads to better results than just using one or the other method. |
Keywords
* Artificial intelligence * Fine tuning * Image denoising * Mask * Pretraining * Self supervised * Supervised
