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New framework solves mode collapse in generative adversarial network - EurekAlert

<p>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 is mode collapse, where the diversity of generated samples is notably lower than that of real samples. The complexity of GANs and their training process has made it difficult to reveal the underlying mechanism of mode collapse.</p> <p>A research team led by Prof. YANG Zhouwang from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences (CAS) conducted a thorough investigation into the root cause of mode collapse and proposed a new framework, Dynamic GAN (DynGAN), to quantitatively detect and resolve mode collapse in GAN. Their work was published in <em>IEEE Transactions on Pattern Analysis and Machine Intelligence</em>.</p>

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