A collaborative mini-research project analyzing Wasserstein GANs (WGANs) through extensive literature review and experimental evaluation. Explores training stability, loss behavior, gradient penalties, and convergence characteristics, proposing insights to improve generative model robustness.
machine-learning research ai deep-learning pytorch gan neural-networks representation-learning unsupervised-learning wgan generative-models gradient-penalty ai-research wasserstein-distance theoretical-ml model-stability
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Updated
Jan 4, 2026