From GAN to WGAN
Par un écrivain mystérieux
Last updated 31 mai 2024
[Updated on 2018-09-30: thanks to Yoonju, we have this post translated in Korean!] [Updated on 2019-04-18: this post is also available on arXiv.]
Generative adversarial network (GAN) has shown great results in many generative tasks to replicate the real-world rich content such as images, human language, and music. It is inspired by game theory: two models, a generator and a critic, are competing with each other while making each other stronger at the same time.
machine learning - Is my loss function right? WGAN - Data Science Stack Exchange
From GAN to WGAN
WGANs: A stable alternative to traditional GANs
WGAN: Wasserstein Generative Adversarial Networks
63 - PyTorch Wasserstein GAN (WGAN) Implementation from scratch, Deep Learning
Lernapparat - Machine Learning
4. Generative Adversarial Networks - Generative Deep Learning [Book]
Generative Adversarial Networks: Create Data from Noise
Advanced Topics in GANs. Want to turn horses into zebras? Make…, by Matthew Stewart, PhD
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