CcGAN-AVAR combines adaptive label vicinities with regression and density-ratio auxiliaries to make continuous conditional GANs robust to data imbalance while retaining one-step sampling.
GAN-based framework for unified estimation of process-induced random variation in FinFET,
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Imbalance-Robust and Sampling-Efficient Continuous Conditional GANs via Adaptive Vicinal Learning and Auxiliary Regularization
CcGAN-AVAR combines adaptive label vicinities with regression and density-ratio auxiliaries to make continuous conditional GANs robust to data imbalance while retaining one-step sampling.