^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 Feature Learning, 2011. ^In search of lost domain generalization. In ...
Domain-adversarial learning is used to train a domain-invariant 2D U-Net using labelled and unlabelled data. This approach is evaluated on both seen and unseen domains from the M&Ms challenge dataset and the domain-adversarial approach shows improved performance as compared to standard training. ...
最后,论文还采用了对抗学习(Adversarial Learning)的方法,参考多任务学习模型[6]中的GRL[7]结构来帮助shared模块学习domain-shared的特征。 Figure 8:GRL有关结构及其整体作用过程 如图8所示,绿色部分为模型的特征抽取器,用于生成特征f,蓝色部分为常用的模型分类功能,生成最后的预测输出与对应的损失 L_y ,而红色部分...
使用对抗学习(Adversarial Training)的UDA(Unsupervised Domain Adaptation)无监督域自适应方法大部分人已经在使用了,但是本文作者发现这些方法没有考量每个域的多模态性质(the multi-modal nature of video within each domain.),即假如我使用其他模态进行协同学习时这种environmental bias会不会变小,或许一种模态下学习的...
We propose a multi-domain adaptation framework for person re-identification.A camera-aware graph is proposed to represent the relationships among features.An adversarial training scheme is proposed to transfer the knowledge.The proposed framework outperforms the state-of-the-art methods on three dataset...
In this paper we propose a hierarchical adversarial neural network (HANN) for adaptive sentiment analysis. Unlike most existing deep learning based methods, the proposed method HANN is able to share information between multiple domains bidirectionally, not just transfers information from source domain to...
As manually labeling the training samples of each band is always time-consuming, we propose an unsupervised multi-level domain adaptation method based on adversarial learning to solve the problem of multi-band SAR images...
Under the framework of mutual learn-ing, the proposed method pairs the target domain with eachsingle source domain to train a conditional adversarial do-main adaptation network as a branch network, while takingthe pair of the combined multi-source domain and targetdomain to train a conditional ...
Semi-supervised learning: [USB: unified semi-supervised learning benchmark] | [TorchSSL: a unified SSL library] LLM benchmark: [PromptBench: adversarial robustness of prompts of LLMs] Federated learning: [PersonalizedFL: library for personalized federated learning] ...
Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning笔记 wastelands 珂学家摘要 首先在图像级和语义级恢复的损失函数下分别综合研究了两种用于对抗性鲁棒性增强的像素去噪方法(即现有的基于加法和未探索的基于过滤的方法),表明与现有的基于像素的基于加法的方法相比,逐像素滤波可以...