Summary of Progressive Monitoring Of Generative Model Training Evolution, by Vidya Prasad et al.
Progressive Monitoring of Generative Model Training Evolution
by Vidya Prasad, Anna Vilanova, Nicola Pezzotti
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 Deep generative models (DGMs), popular for their ability to generate realistic data, are prone to biases and inefficiencies. As DGMs become increasingly complex, monitoring their training process becomes crucial to achieve desired results and optimize resources. Our progressive analysis framework achieves this by utilizing dimensionality reduction techniques to inspect latent representations, generated distributions, and real distributions as they evolve during training iterations. This monitoring allows for the detection of undesirable outcomes, enabling timely intervention to fix issues and minimize computational loads. We demonstrate our method’s effectiveness in identifying and mitigating biases early in training a Generative Adversarial Network (GAN) and improving the quality of the generated data distribution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how machines can learn to create new data that looks real. But sometimes, these machines can get stuck or make mistakes. To fix this, we created a special way to look at how the machine is learning as it goes along. This helps us catch any problems early and stop them from getting worse. We tested our method with a type of machine called a Generative Adversarial Network (GAN) and showed that it can help create better data. The goal is to make sure machines can learn in a way that makes sense and doesn’t make mistakes. |
Keywords
» Artificial intelligence » Dimensionality reduction » Gan » Generative adversarial network
