Summary of Early Stopping Criteria For Training Generative Adversarial Networks in Biomedical Imaging, by Muhammad Muneeb Saad et al.
Early Stopping Criteria for Training Generative Adversarial Networks in Biomedical Imaging
by Muhammad Muneeb Saad, Mubashir Husain Rehmani, Ruairi O’Reilly
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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 In this paper, researchers aim to reduce the computational costs associated with training Generative Adversarial Networks (GANs) for biomedical imagery. Current methods involve manual qualitative analysis of loss values and synthetic image diversity, which can be time-consuming and prone to errors. The proposed novel early stopping criteria uses a combination of generator and discriminator loss values, Mean Structural Similarity Index (MS-SSIM), and Fréchet Inception Distance (FID) scores to detect training problems such as mode collapse, non-convergence, and instability. This approach can reduce the computational costs and training time required to generate diversified and high-quality synthetic images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GANs are a type of artificial intelligence that helps create new images or data. However, they require a lot of computer power and human oversight to work properly. Researchers have been trying to find ways to make GANs more efficient. One way is by stopping the training process when it’s not getting better anymore. But this can be tricky because GANs can get stuck in a bad state called “mode collapse” or “non-convergence.” To solve this problem, scientists are looking for new methods that can automatically detect when the training is going wrong and stop it before wasting too much computer power. |
Keywords
» Artificial intelligence » Early stopping
