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Estimation with Uncertainty via Conditional Generative Adversarial Networks
Such a deterministic nature in ANNs causes the limitations of using ANNs for medical diagnosis, law problems, and portfolio management, in which discovering not only the prediction but also the uncertainty of the prediction is essentially required.
Regularization Methods for Generative Adversarial Networks: An Overview of Recent Studies
However, applying GAN to different data types with diverse neural network architectures has been hindered by its limitation in training, where the model easily diverges.
Score-Guided Generative Adversarial Networks
We propose a Generative Adversarial Network (GAN) that introduces an evaluator module using pre-trained networks.
Controllable Generative Adversarial Network
The essential task of GAN is to control the features of samples generated from a random distribution.
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