Generative Adversarial Network (GAN) is widely used to synthesize intricate and realistic data by learning the distribution of authentic real samples. However, a significant challenge that GAN faces ...
Researchers from Chung-Ang University propose a strategy that addresses the stability and efficiency issues of GANs, boosting their performance while also being flexible enough to adapt to different ...
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