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域自适应论文和代码 这个仓库是关于深度领域适应和领域概括的论文和代码。 目录 NIPS 耦合生成对抗网络 域分离网络 ICML CyCADA：周期一致的对抗域自适应 [Pytorch（官方）] 学习用于无监督域自适应的语义表示[2018] [TensorFlow（官方）] 心肺复苏术 生成适应：使用生成对抗网络对齐域[2018] [Pytorch（官方）] 使用选择性对抗网络进行部分转移学习” [2018] [Caffe和Pytorch（官方）] 对抗性区分域适应[2017] [Tensorflow（Official）] [Pytorch] 国际CCV 开放集域适配[2017] [MATLAB（官方）] AAAI 多专业域适配[2018] [Caffe（官方）] ECCV 通过反向传播进行开放集域适应[2018] [Tensorflow] arXiv

丹恩 PyTorch实施DANN（神经网络领域-专业训练） “” “” 先决条件 python 3.5 pytorch 0.4.1 结果 MNIST -> MNIST-M 这项工作的结果来自平均（以下五项测试）。 实验 仅来源 丹恩 MNIST和MNIST-M功能分布 仅来源 域适应（DANN）

近期，一些Paper放出来，Domain Adaptation（域自适应）相关研究非常火热，特别是基于Domain Adaptation的视觉应用在今年的CVPR中有不少。这里，整理了CVPR 2020 域自适应（DA）相关的比较有意思的值得阅读的六篇论文，供大家参考—行为分割、语义分割、目标检测、行为识别、域自适应检索。

PyTorch中的域自适应快速R-CNN 这是Haoran Wang（ ）实施的“用于野外物体检测的域自适应快速R-CNN”的PyTorch实现。 原始文件可以在找到。 此实现基于 @ 。 如果您发现此存储库有用，请引用以下原始论文： @inproceedings{chen2018domain, title={Domain Adaptive Faster R-CNN for Object Detection in the Wild}, author = {Chen, Yuhua and Li, Wen and Sakaridis, Christos and Dai, Dengxin and Van Gool, Luc}, booktitle = {Computer Vision and Pattern Recognition (CVPR)},

Domain adaptation is an active, emerging research area that attempts to address the changes in data distribution across training and testing datasets. With the availability of a multitude of image acquisition sen- sors, variations due to illumination, and viewpoint among others, com- puter vision applications present a very natural test bed for evaluating domain adaptation methods. In this monograph, we provide a compre- hensive overview of domain adaptation solutions for visual recognition problems. By starting with the problem description and illustrations, we discuss three adaptation scenarios namely, (i) unsupervised adap- tation where the “source domain” training data is partially labeled and the “target domain” test data is unlabeled, (ii) semi-supervised adaptation where the target domain also has partial labels, and (iii) multi-domain heterogeneous adaptation which studies the previous two settings with the source and/or target having more than one domain, and accounts for cases where the features used to represent the data in each domain are different. For all these topics we discuss existing adaptation techniques in the literature, which are motivated by the principles of max-margin discriminative learning, manifold learning, sparse coding, as well as low-rank representations. These techniques have shown improved performance on a variety of applications such as object recognition, face recognition, activity analysis, concept clas- sification, and person detection. We then conclude by analyzing the challenges posed by the realm of “big visual data”, in terms of the generalization ability of adaptation algorithms to unconstrained data acquisition as well as issues related to their computational tractability, and draw parallels with the efforts from vision community on image transformation models, and invariant descriptors so as to facilitate im- proved understanding of vision problems under uncertainty.

Unsupervised Domain Adaptation by Backpropagation.pdf