Summary of Normalizing Flow-based Metric For Image Generation, by Pranav Jeevan et al.
Normalizing Flow-Based Metric for Image Generation
by Pranav Jeevan, Neeraj Nixon, Amit Sethi
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 introduces two novel evaluation metrics for assessing the realism of generated images based on normalizing flows: Flow-based Likelihood Distance (FLD) and Dual-Flow Based Likelihood Distance (D-FLD). Unlike Fréchet Inception Distance (FID), these metrics do not require a large number of real images to stabilize, making them suitable for small validation batches. The proposed metrics also have fewer parameters compared to FID’s Inception-V3 network, making them computationally more efficient. For assessing generated images in new domains, the smaller network is advantageous as it can be retrained on real images to model distinct distributions. The paper demonstrates the effectiveness of these metrics through extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research proposes two new ways to measure how realistic computer-generated images are. These methods use a type of math called “normalizing flows” to compare generated images with real ones. One advantage is that they can be used with smaller sets of images, making them more practical for everyday use. Another benefit is that the calculations require less computational power than other popular methods. The researchers tested these new metrics and found that they accurately reflect how well or poorly generated images are. |
Keywords
» Artificial intelligence » Likelihood
