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为此,作者在GAN网络中加入了Encoder组成了Bidirectional Generative Adversarial Networks (BiGANs,双向GAN),让其中的特征表示也参与了鉴别,改善了表示的质量。 Adversarial Feature Learning (ICLR2017) Method:通常的GAN只鉴别数据空间,例如生成图像G(z)和原始图像x,这里还让Generator的输入z和输出G(z)组成元组,Encoder...
To tackle the above problems, we adapt the contrastive learning scheme to adversarial examples for robustness enhancement, and also extend the self-supervised contrastive approach to the supervised setting for the ability to discriminate on classes. Equipped with our new designs, we proposed adversarial...
生成方法通过从未标记的数据中生成合成样本来进行自监督学习。例如,使用自动编码器 (autoencoders) 或生成对抗网络 (generative adversarial networks, GANs) 来重构或生成与原始图像相似的图像。模型通过学习将图像转换为低维表示,并再次还原回原始图像,从而学习图像的有用特征。 预测方法 (Predictive methods): 预测方法...
generative, contrastive, 和 generative-contrastive (adversarial)三个部分各自的发展,以及最近生成模型向contrastive的转变过程。 提供了自监督学习方法的理论可靠性,并展示了它如何有益于下游的监督学习任务。理论可靠性还是很make sense的 提出了目前自监督学习还存在的一些问题以及未来可能的发展方向 ...
Supervised Contrastive Learning:有监督对比学习 1 概要 交叉熵损失是监督学习中应用最广泛的损失函数,度量两个分布(标签分布和经验回归分布)之间的KL散度,但是也存在对于有噪声的标签缺乏鲁棒性、可能存在差裕度(允许有余地的余度)导致泛化性能下降的问题。而大多数替代方案还不能很好地用于像ImageNet这样的大规模数据...
& Hwang S. Adversarial Self-Supervised Contrastive Learning. In Advances in Neural Information Processing Systems, 2020.概这篇文章提出了对比学习结合adversarial training的一个思路.主要内容对比学习的强大之处在于正负样本对的构造, 一个结合adversarial training的很自然的思路是, 将普通样本与其相对应的对抗样本...
Semi-supervised learning is a typical and promising framework for this strategy. For example, self-training [42] and co-training [43] have been explored for semi-supervised classification of HSIs. In addition, the generative adversarial network (GAN) has also been widely used for HSI ...
Learning imbalanced datasets with label-distribution-aware margin loss29 A Bayesian/information theoretic model of learning to learn via multiple task sampling30 Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples31 ...
SMOOTHING WITH VIRTUAL ADVERSARIALTRAININGADVERSARIALTRAININGMETHODSFORSEMI-SUPERVISEDTEXTCLASSIFICATIONFREELB: ENHANCED ADVERSARIALTRAININGFORLANGUAGE UNDERSTANDING Attack: Defense:In 半监督学习 半监督学习 Semi-supervisedLearningforGenerative Model Low-density Separation Assumption 非黑即白 self-traininghard-label vs ...