HOGDA: Boosting Semi-supervised Graph Domain Adaptation via High-Order Structure-Guided Adaptive Feature Alignment

Semi-supervised visual domain adaptation is devoted to adapting a model learned in source domain to target domain where there are only a few labeled samples. In this paper, we propose a semi-supervised cross-domain image recognition method which unifies the feature learning and recognition model training into a convolutional neural network framework. Based on a few labeled samples and massive unlabeled samples in the source and target domains, we specially design three branches for class label, domain label and similarity label prediction which simultaneously optimizes the network to generate image features that are domain invariance and inter-class discriminative. Experimental results demonstrate that our method is effective for learning robust cross-domain image recognition model, and achieves the state-of-the-art performance on the widely used visual domain adaptation benchmark.

Domain adaptation (DA) is a representation learning methodology that transfers knowledge from a label-sufficient source domain to a label-scarce target domain. While most of early methods are focused on unsupervised DA (UDA), several studies on semi-supervised DA (SSDA) are recently suggested. In SSDA, a small number of labeled target images are given for training, and the effectiveness of those data is demonstrated by the previous studies. However, the previous SSDA approaches solely adopt those data for embedding ordinary supervised losses, overlooking the potential usefulness of the few yet informative clues. Based on this observation, in this paper, we propose a novel method that further exploits the labeled target images for SSDA. Specifically, we utilize labeled target images to selectively generate pseudo labels for unlabeled target images. In addition, based on the observation that pseudo labels are inevitably noisy, we apply a label noise-robust learning scheme, which progressively updates the network and the set of pseudo labels by turns. Extensive experimental results show that our proposed method outperforms other previous state-of-the-art SSDA methods.

Medical image annotations are prohibitively time-consuming and expensive to obtain. To alleviate annotation scarcity, many approaches have been developed to efficiently utilize extra information, e.g., semi-supervised learning further exploring plentiful unlabeled data, domain adaptation including multi-modality learning and unsupervised domain adaptation resorting to the prior knowledge from additional modality. In this paper, we aim to investigate the feasibility of simultaneously leveraging abundant unlabeled data and well-established cross-modality data for annotation-efficient medical image segmentation. To this end, we propose a novel semi-supervised domain adaptation approach, namely Dual-Teacher, where the student model not only learns from labeled target data (e.g., CT), but also explores unlabeled target data and labeled source data (e.g., MR) by two teacher models. Specifically, the student model learns the knowledge of unlabeled target data from intra-domain teacher by encouraging prediction consistency, as well as the shape priors embedded in labeled source data from inter-domain teacher via knowledge distillation. Consequently, the student model can effectively exploit the information from all three data resources and comprehensively integrate them to achieve improved performance. We conduct extensive experiments on MM-WHS 2017 dataset and demonstrate that our approach is able to concurrently utilize unlabeled data and cross-modality data with superior performance, outperforming semi-supervised learning and domain adaptation methods with a large margin.

In this paper, we study weakly supervised learning where a large amount of data supervision is not accessible. This includes i) incomplete supervision, where only a small subset of labels is given, such as semi-supervised learning and domain adaptation; ii) inexact supervision, where only coarse-grained labels are given, such as multi-instance learning and iii) inaccurate supervision, where the given labels are not always ground-truth, such as label noise learning. Unlike supervised learning which typically achieves performance improvement with more labeled examples, weakly supervised learning may sometimes even degenerate performance with more weakly supervised data. Such deficiency seriously hinders the deployment of weakly supervised learning to real tasks. It is thus highly desired to study safe weakly supervised learning, which never seriously hurts performance. To this end, we present a generic ensemble learning scheme to derive a safe prediction by integrating multiple weakly supervised learners. We optimize the worst-case performance gain and lead to a maximin optimization. This brings multiple advantages to safe weakly supervised learning. First, for many commonly used convex loss functions in classification and regression, it is guaranteed to derive a safe prediction under a mild condition. Second, prior knowledge related to the weight of the base weakly supervised learners can be flexibly embedded. Third, it can be globally and efficiently addressed by simple convex quadratic or linear program. Finally, it is in an intuitive geometric interpretation with the least square loss. Extensive experiments on various weakly supervised learning tasks, including semi-supervised learning, domain adaptation, multi-instance learning and label noise learning demonstrate our effectiveness.

Annotation scarcity is a long-standing problem in medical image analysis area. To efficiently leverage limited annotations, abundant unlabeled data are additionally exploited in semi-supervised learning, while well-established cross-modality data are investigated in domain adaptation. In this paper, we aim to explore the feasibility of concurrently leveraging both unlabeled data and cross-modality data for annotation-efficient cardiac segmentation. To this end, we propose a cutting-edge semi-supervised domain adaptation framework, namely Dual-Teacher++. Besides directly learning from limited labeled target domain data (e.g., CT) via a student model adopted by previous literature, we design novel dual teacher models, including an inter-domain teacher model to explore cross-modality priors from source domain (e.g., MR) and an intra-domain teacher model to investigate the knowledge beneath unlabeled target domain. In this way, the dual teacher models would transfer acquired inter- and intra-domain knowledge to the student model for further integration and exploitation. Moreover, to encourag reliable dual-domain knowledge transfer, we enhance the inter-domain knowledge transfer on the samples with higher similarity to target domain after appearance alignment, and also strengthen intra-domain knowledge transfer of unlabeled target data with higher prediction confidence. In this way, the student model can obtain reliable dual-domain knowledge and yield improved performance on target domain data. We extensively evaluated the feasibility of our method on the MM-WHS 2017 challenge dataset. The experiments have demonstrated the superiority of our framework over other semi-supervised learning and domain adaptation methods. Moreover, our performance gains could be yielded in bidirections, i.e., adapting from MR to CT, and from CT to MR. Our code will be available at https://github.com/kli-lalala/Dual-Teacher-.

Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks

Exploiting node-feature bipartite graph in graph convolutional networks

Rapid domain adaptation for disaster impact assessment: Remote sensing of building damage after the 2021 Germany floods

Semi-supervised domain adaptation (SDA) is a typical setting when we face the problem of domain adaptation in real applications. How to effectively utilize the unlabeled data is an important issue in SDA. Previous work requires access to the source data to measure the data distribution mismatch, which is ineffective when the size of the source data is relatively large. In this paper, we propose a new paradigm, called Generalized Distillation Semi-supervised Domain Adaptation (GDSDA). We show that without accessing the source data, GDSDA can effectively utilize the unlabeled data to transfer the knowledge from the source models. Then we propose GDSDA-SVM which uses SVM as the base classifier and can efficiently solve the SDA problem. Experimental results show that GDSDA-SVM can effectively utilize the unlabeled data to transfer the knowledge between different domains under the SDA setting.

Semi-supervised domain adaptation (SSDA) aims to adapt models trained from a labeled source domain to a different but related target domain, from which unlabeled data and a small set of labeled data are provided. Current methods that treat source and target supervision without distinction overlook their inherent discrepancy, resulting in a source-dominated model that has not effectively use the target supervision. In this paper, we argue that the labeled target data needs to be distinguished for effective SSDA, and propose to explicitly decompose the SSDA task into two sub-tasks: a semi-supervised learning (SSL) task in the target domain and an unsupervised domain adaptation (UDA) task across domains. By doing so, the two sub-tasks can better leverage the corresponding supervision and thus yield very different classifiers. To integrate the strengths of the two classifiers, we apply the well established co-training framework, in which the two classifiers exchange their high confident predictions to iteratively "teach each other" so that both classifiers can excel in the target domain. We call our approach Deep Co-training with Task decomposition (DeCoTa). DeCoTa requires no adversarial training and is easy to implement. Moreover, DeCoTa is well founded on the theoretical condition of when co-training would succeed. As a result, DeCoTa achieves state-of-the-art results on several SSDA datasets, outperforming the prior art by a notable 4% margin on DomainNet. Code is available at https://github.com/LoyoYang/DeCoTa.

Recently, Semi-supervised Domain Adaptation (SSDA) has become more practical because a small number of labeled target samples can significantly boost the empirical target performance when using SSDA. Several current methods focus on prototype-based alignment to achieve cross-domain invariance in which the labeled samples from the source and target domains are concatenated to estimate the prototypes. The model is then trained to assign the unlabeled target data to the prototype within the same class. However, such methods fail to exploit the advantage of using few labeled target data because the labeled source data dominate the prototypes in the supervision process. Moreover, a recent method [1] showed that concatenating source and target samples for training can damage the semantic information of representations, which degrades the trained model’s ability to generate discriminative features. To solve these problems, in this paper, we divide labeled source and target samples into two subgroups for training. One group includes a large number of labeled source samples, and the other obtains a few labeled target samples. Then, we propose a novel SSDA framework that consists of two models. A model trained on the group that has the labeled source samples to provide an “inter-view” on the unlabeled target data is called the inter-view model. A model trained on a few labeled target samples that provides an “intra-view” of the unlabeled target data is called the intra-view model. Finally, both of these models collaborate to fully exploit information on the unlabeled target data. To the best of our knowledge, our proposed method achieves the state-of-the-art classification performance of SSDA in extensive experiments conducted on several visual benchmark domain adaptation datasets that utilize the advantages of multiple views and collaborative training.

Graph convolutional neural networks have difficulties in model training takes a lot of time and lack of universality in meta-path design when processing knowledge graphs. To solve these issues, this paper proposes an efficient recommendation algorithm integrating knowledge graphs with graph convolutional networks(EKG-GCN). The algorithm is mainly based on the graph convolution algorithm. Firstly, the relationship between users and entities is standardized and scored, then a new neighborhood aggregation method is proposed. Some nodes in the knowledge graph are set as central nodes. A distance-influence function is set up according to the distance from other nodes to the central node, and the feature extraction of information in the knowledge graph is completed according to the distance-influence function. Finally, the knowledge graph features are used as an auxiliary to complete the recommendation task. In the experiment, three data sets were selected to compare with other advanced models, and it was found that the model is in an advantageous position in terms of training time and recommendation effect, and has a certain degree of universality.

Wheel condition assessment of high-speed trains under various operational conditions using semi-supervised adversarial domain adaptation

Graph convolutional networks (GCNs) have achieved impressive success in many graph related analytics tasks. However, most GCNs only work in a single domain (graph) incapable of transferring knowledge from/to other domains (graphs), due to the challenges in both graph representation learning and domain adaptation over graph structures. In this paper, we present a novel approach, unsupervised domain adaptive graph convolutional networks (UDA-GCN), for domain adaptation learning for graphs. To enable effective graph representation learning, we first develop a dual graph convolutional network component, which jointly exploits local and global consistency for feature aggregation. An attention mechanism is further used to produce a unified representation for each node in different graphs. To facilitate knowledge transfer between graphs, we propose a domain adaptive learning module to optimize three different loss functions, namely source classifier loss, domain classifier loss, and target classifier loss as a whole, thus our model can differentiate class labels in the source domain, samples from different domains, the class labels from the target domain, respectively. Experimental results on real-world datasets in the node classification task validate the performance of our method, compared to state-of-the-art graph neural network algorithms.

Source-free semi-supervised domain adaptation via progressive Mixup