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> ML_ALGORITHM // WASSERSTEIN-GAN-GP_v1.0

Wasserstein GAN with Gradient Penalty (WGAN-GP)

Robust GAN variant replacing Jensen-Shannon divergence with the Wasserstein metric enforced through a 1-Lipschitz gradient penalty on interpolated points.

Adversarial Generative Networksdeep-generativeblack-boxlarge (>100k)
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Computational Complexity
Training Complexity:O(epochs * batch_size * (gen + 5 * critic))
Inference Complexity:O(gen_forward)
Hardware Profile
CPU Friendly:No
Requires GPU:Yes
Memory Footprint:high
Interpretability & Data
Interpretability Tier:black-box
Training Data Needs:large (>100k)

Interpretability Assessment

Critic loss correlates monotonically with image quality, providing a reliable convergence metric.

Suitable Tasks & Supported Modalities

Suitable Tasks:
image generationdomain adaptation
Supported Modalities:
imagetabular

Implementing Libraries

PyTorchLinux Foundation / PyTorch Foundation · v2.4.1
View Spec
kerashub

Foundational Literature

Common Pitfalls & Warnings
  • Batch normalization cannot be used in the critic because gradient penalty is computed with respect to individual samples independently