University of Pittsburgh

Generating High-Quality image for weakly supervised learning, semi-supervised learning, and transfer learning via conditional Generative Adversarial Network.

Friday, March 6, 2020 - 1:00pm - 1:30pm

Abstract: Conditional generative models enjoy remarkable progress over the past few years. One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN), which generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier. However, the diversity of the generated samples by AC-GAN tends to decrease as the number of classes increases, hence limiting its power on large-scale data. To address this issue, we identify the source of the low diversity theoretically. We propose Twin Auxiliary Classifiers Generative Adversarial Net (TAC-GAN) that further benefits from a new player that interacts with other players (the generator and the discriminator) in GAN. Additionally, we also study the application of our model on the weak learning (learning from complementary labeled data), semi-supervised learning (learning from unlabeled data) and transfer learning (domain adaptation).

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