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cs.LG2023
Learning with Complementary Labels Revisited: The Selected-Completely-at-Random Setting Is More Practical
Wei Wang, Takashi Ishida, Yu-Jie Zhang +2
Complementary-label learning is a weakly supervised learning problem in which each training example is associated with one or multiple complementary labels indicating the classes t…
cs.LG2023★ 1 cited
Flooding Regularization for Stable Training of Generative Adversarial Networks
Iu Yahiro, Takashi Ishida, Naoto Yokoya
Generative Adversarial Networks (GANs) have shown remarkable performance in image generation. However, GAN training suffers from the problem of instability. One of the main approac…