Summary of Stable Diffusion Dataset Generation For Downstream Classification Tasks, by Eugenio Lomurno et al.
Stable Diffusion Dataset Generation for Downstream Classification Tasks
by Eugenio Lomurno, Matteo D’Oria, Matteo Matteucci
First submitted to arxiv on: 4 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 explores the adaptation of the Stable Diffusion 2.0 model for generating synthetic datasets using Transfer Learning, Fine-Tuning, and generation parameter optimisation techniques. The authors present a class-conditional version of the model that exploits a Class-Encoder and optimises key generation parameters to improve the utility of the dataset for downstream classification tasks. By leveraging these techniques, the authors show that in a third of cases, synthetic datasets can outperform those trained on real-world data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates fake data that’s really good at pretending to be real. It uses a special model called Stable Diffusion 2.0 and some clever tricks to make it better at making fake data. They’re trying to see if they can use this fake data to train machines to do tasks, like recognizing pictures or understanding language. The results are promising – sometimes the fake data is actually better than real data! |
Keywords
» Artificial intelligence » Classification » Diffusion » Encoder » Fine tuning » Transfer learning
