GAN vs WGAN-GP Training-Loss Variability

Training-loss variability (standard deviation) metrics for Generative Adversarial Networks (GANs) and Wasserstein GANs with Gradient Penalty (WGAN-GP). Xiao Liao authored this dataset, which was last updated on May 21, 2026. The dataset is a 5.5 KB Excel file.

Use Cases

• Compare training stability between GAN and WGAN-GP architectures based on loss variability metrics
• Analyze convergence patterns in generative adversarial networks based on loss standard deviation
• Benchmark model performance for generative tasks based on training-loss statistics

Strengths

• Dataset is openly licensed under CC-BY-4.0.
• File size is 5.5 KB, indicating a compact and focused dataset.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Xiao Liao

Freshness

Last updated 2026-05-21 17:42:21; freshness should be verified.