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Note:[ wechat:Y466551 | 可加勿骚扰,付费咨询 ] 论文信息 论文标题:Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning论文作者:T 阅读全文
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交叉熵公式: $H(p, q)=-\sum_{x} p(x) \log q(x)$ 其中: $p$ 代表真实分布; $q$ 代表拟合分布; 代码: # Example of target with class indices loss = nn.CrossEntropyLoss() input = 阅读全文
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论文信息 论文标题:Do We Need Zero Training Loss After Achieving Zero Training Error?论文作者:Takashi Ishida, I. Yamane, Tomoya Sakai, Gang Niu, M. Sugiyama论文来源:20 阅读全文