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This chapter presents an introduction to the evaluation indicators and visualization aspects of GANs. The evaluation indicators covered mainly include methods such as Inception Score (IS), Mode Score (MS), modified Inception Score (m-IS), Frechet Inception Distance (FID), Maximum Mean Discrepancy (MMD), Wasserstein distance, the nearest neighbor classifier, GANtrain and GANtest, Nearest Real Data Similarity (NRDS), Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Sharpness Difference. Additionally, an open-source tool named GAN Lab is introduced. This tool provides a visual representation of the training process, data flow, and working principles of GANs.

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Evaluation and Visualization of GAN

  • 章节

  • 发表日期：

    2026年

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  • Peng Long,
  • Xiaozhou Guo

摘要

This chapter presents an introduction to the evaluation indicators and visualization aspects of GANs. The evaluation indicators covered mainly include methods such as Inception Score (IS), Mode Score (MS), modified Inception Score (m-IS), Frechet Inception Distance (FID), Maximum Mean Discrepancy (MMD), Wasserstein distance, the nearest neighbor classifier, GANtrain and GANtest, Nearest Real Data Similarity (NRDS), Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Sharpness Difference. Additionally, an open-source tool named GAN Lab is introduced. This tool provides a visual representation of the training process, data flow, and working principles of GANs.