Summary of Transferring Disentangled Representations: Bridging the Gap Between Synthetic and Real Images, by Jacopo Dapueto et al.
Transferring disentangled representations: bridging the gap between synthetic and real images
by Jacopo Dapueto, Nicoletta Noceti, Francesca Odone
First submitted to arxiv on: 26 Sep 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 proposed paper explores the potential of developing general-purpose disentangled representations through the transfer learning of synthetic data to real images. By investigating the effects of fine-tuning and analyzing the preservation of properties such as disentanglement quality, the authors demonstrate that some level of disentanglement is possible and effective when transferring representations from synthetic to real data. The study also introduces a new interpretable intervention-based metric for measuring factor encoding in the representation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make meaningful and efficient representations by understanding how data was created. But it’s hard because there are things that happen together, like image features, and we can’t always know what’s “right”. The researchers ask if we can use fake images to learn about real ones, and they test this idea with lots of experiments. They also come up with a new way to measure how well the representation captures different parts of an image. |
Keywords
» Artificial intelligence » Fine tuning » Synthetic data » Transfer learning
