通过域对抗训练 (Domain adversarial training: DAT) 最小化域分歧(domain divergence),在 DA/DG 任务中显示出了很好的的泛化性能。如下图所示,我们在 backbone+classifier 的基础上增加一个域分类器来对特征归属的域进行分类,在梯度反传至 backbone 时将符号取负,以此训练 backbone 让他的特征与域信息无关,只与...
领域对抗神经网络 (Domain Adversarial Neural Network,DANN)[2]是域自适应使用最为广泛的方法之一。它的核心想法就是在表示层面减少边缘分布和的差异。 生成对抗网络 Generative Adversarial Net(GAN)[3]引入了一个判别器来刻画真实的数据分布和生成的数据分布之间的差异。受到 GAN 的启发,DANN 使用域判别器(Domain ...
没错 Domain Adversarial Training,就非常像是 Gan,你可以把 Feature Extractor,想成是 Generator,把 Domain Classifier,想成是 Discriminator,那其实 Domain Adversarial Training,最早的 Paper,我记得是发表在 2015 年的 ICML 上面,比那个 Gan 还要稍微晚一点点啦,不过它们几乎可以说是同时期的作品,在 Domain...
Measurement, judgment and decision making, pages 179–250, 1998. ^Domain-adversarial training of neural networks. Journal of machine learning research, 17(1):2096–2030, 2016. ^ Reading digits in natural images with unsupervised feature learning. NIPS Workshop on Deep Learning and Unsupervised Feat...
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The official codes of paper:Adversarial Style Augmentation for Domain Generalization One Sentence Summary:AdvStyle explores a broader style space over MixStyle, DSU, and EFDMix by searching for the most challenging domains via adversarial training. ...
The adversarial network D introduced in Section 3.2 has the same architecture as the one used in DCGAN 实验结果1 实验结果2 5.概念介绍: 1.用于语义分割的非监督域适配的对抗训练 Adversarial training for UDA is the most explored approach for semantic segmentation. It involves two networks.One network...
will then be optimized to minimize the estimated Wasserstein distance in an adversarial manner. By iterative adversarial training, we finally learn feature representations invariant to the covariate shift between domains. WDGRL训练一个领域判别网络来估计源和目标特征表示之间的Wasserstein distance。特征提取器网...
Domain-Adversarial Training of Neural Networks[JMLR2016] Unsupervised Domain Adaptation by Backpropagation[ICML2015][Caffe(Official)][Tensorflow][Pytorch] Network Methods Boosting Domain Adaptation by Discovering Latent Domains[CVPR2018] Residual Parameter Transfer for Deep Domain Adaptation[CVPR2018] ...
et al. [63] designed a novel unsupervised adversarial contrastive learning method to pre-train a CNN-based Siamese network, which minimized the feature similarity of augmented data and its corresponding unsupervised adversarial samples. Through the designed pretext task, [63] obtained competitive ...