To transfer the knowledge learned from a labeled source domain to an
unlabeled target domain, many studies have worked on universal domain
adaptation (UniDA), where there is no constraint on the label sets of the
source domain and target domain. However, the existing UniDA methods rely on
source samples with correct annotations. Due to the limited resources in the
real world, it is difficult to obtain a large amount of perfectly clean labeled
data in a source domain in some applications. As a result, we propose a novel
realistic scenario named Noisy UniDA, in which classifiers are trained using
noisy labeled data from the source domain as well as unlabeled domain data from
the target domain that has an uncertain class distribution. A multi-head
convolutional neural network framework is proposed in this paper to address all
of the challenges faced in the Noisy UniDA at once. Our network comprises a
single common feature generator and multiple classifiers with various decision
bounds. We can detect noisy samples in the source domain, identify unknown
classes in the target domain, and align the distribution of the source and
target domains by optimizing the divergence between the outputs of the various
classifiers. The proposed method outperformed the existing methods in most of
the settings after a thorough analysis of the various domain adaption
scenarios. The source code is available at
https://github.com/YU1ut/Divergence-Optimization.

本文提出了一个名为 “不清晰统一域适应” 的新颖场景，以应对源域中存在噪声数据且目标域中存在未知类别分布的情况。文章将多标头卷积神经网络作为解决该场景下在同一模型中同时解决存在噪声数据、未知类别与源域 / 目标域分布不同等挑战的方法，并在多个场景下验证了该方法的有效性和优越性

通过发散最优化实现视觉识别的噪声通用领域自适应

Noisy Universal Domain Adaptation via Divergence Optimization for Visual  Recognition

In many real-world datasets, like WebVision, the performance of DNN based
classifier is often limited by the noisy labeled data. To tackle this problem,
some image related side information, such as captions and tags, often reveal
underlying relationships across images. In this paper, we present an efficient
weakly supervised learning by using a Side Information Network (SINet), which
aims to effectively carry out a large scale classification with severely noisy
labels. The proposed SINet consists of a visual prototype module and a noise
weighting module. The visual prototype module is designed to generate a compact
representation for each category by introducing the side information. The noise
weighting module aims to estimate the correctness of each noisy image and
produce a confidence score for image ranking during the training procedure. The
propsed SINet can largely alleviate the negative impact of noisy image labels,
and is beneficial to train a high performance CNN based classifier. Besides, we
released a fine-grained product dataset called AliProducts, which contains more
than 2.5 million noisy web images crawled from the internet by using queries
generated from 50,000 fine-grained semantic classes. Extensive experiments on
several popular benchmarks (i.e. Webvision, ImageNet and Clothing-1M) and our
proposed AliProducts achieve state-of-the-art performance. The SINet has won
the first place in the classification task on WebVision Challenge 2019, and
outperformed other competitors by a large margin.

通过使用 SINet 实现弱监督学习，该模型通过引入侧面信息生成紧凑的分类表示并估计每个噪声图像的正确性得分，可以大大减少噪声图像标签的负面影响，并有助于训练具有高性能的 CNN 分类器。

用辅助信息进行弱监督学习以应对嘈杂标记图像

Weakly Supervised Learning with Side Information for Noisy Labeled  Images

In unsupervised domain adaptation (UDA), classifiers for the target domain
(TD) are trained with clean labeled data from the source domain (SD) and
unlabeled data from TD. However, in the wild, it is difficult to acquire a
large amount of perfectly clean labeled data in SD given limited budget. Hence,
we consider a new, more realistic and more challenging problem setting, where
classifiers have to be trained with noisy labeled data from SD and unlabeled
data from TD -- we name it wildly UDA (WUDA). We show that WUDA ruins all UDA
methods if taking no care of label noise in SD, and to this end, we propose a
Butterfly framework, a powerful and efficient solution to WUDA. Butterfly
maintains four deep networks simultaneously, where two take care of all
adaptations (i.e., noisy-to-clean, labeled-to-unlabeled, and
SD-to-TD-distributional) and then the other two can focus on classification in
TD. As a consequence, Butterfly possesses all the conceptually necessary
components for solving WUDA. Experiments demonstrate that, under WUDA,
Butterfly significantly outperforms existing baseline methods.

本论文提出了一种新的领域自适应问题，即在源域有噪声标签的情况下，通过未标记的目标域数据进行分类，该方法名为 WUDA。我们建议使用蝴蝶框架，该框架同时维护四个深度网络以解决噪声数据和分布偏移。我们发现，蝴蝶框架在 WUDA 下显着优于现有的基准方法。