Unsupervised Domain Adaptation Research Articles

Rethinking confidence scores for source-free unsupervised domain adaptation

Rethinking confidence scores for source-free unsupervised domain adaptation

Unsupervised Domain Adaptation Depth Estimation Based on Self-attention Mechanism and Edge Consistency Constraints

In the unsupervised domain adaptation (UDA) (Akada et al. Self-supervised learning of domain invariant features for depth estimation, in: 2022 IEEE/CVF winter conference on applications of computer vision (WACV), pp 3377–3387 (2022). 10.1109/WACV51458.2022.00107) depth estimation task, a new adaptive approach is to use the bidirectional transformation network to transfer the style between the target and source domain inputs, and then train the depth estimation network in their respective domains. However, the domain adaptation process and the style transfer may result in defects and biases, often leading to depth holes and instance edge depth missing in the target domain’s depth output. To address these issues, We propose a training network that has been improved in terms of model structure and supervision constraints. First, we introduce a edge-guided self-attention mechanism in the task network of each domain to enhance the network’s attention to high-frequency edge features, maintain clear boundaries and fill in missing areas of depth. Furthermore, we utilize an edge detection algorithm to extract edge features from the input of the target domain. Then we establish edge consistency constraints between inter-domain entities in order to narrow the gap between domains and make domain-to-domain transfers easier. Our experimental demonstrate that our proposed method effectively solve the aforementioned problem, resulting in a higher quality depth map and outperforming existing state-of-the-art methods.

Multimodal Unsupervised Domain Adaptation for Predicting Speaker Characteristics from Video

Multimodal Unsupervised Domain Adaptation for Predicting Speaker Characteristics from Video

A review of optic disc and optic cup segmentation based on fundus images

AbstractOptic disc (OD) and optic cup (OC) segmentation is an important task in ophthalmic medicine and is crucial for aiding glaucoma screening. With the development of smart healthcare and the increase of large datasets, there is an increasing number of research efforts targeting OD and OC segmentation, making it particularly important to provide a systematic review of the latest advances in the field. This paper presents a systematic review of commonly used datasets, evaluation metrics, and related research results in the field of OD and OC segmentation. The advantages and disadvantages of segmentation techniques based on traditional and deep learning methods are comparatively analysed. In addition, this study emphasizes the importance of OD and OC segmentation efforts in smart healthcare. Despite the technological advances, the lack of generalization capability is still a major obstacle limiting its clinical application. To address this issue, this study explores unsupervised domain adaptation methods to enhance the generalization performance of segmentation techniques and provide new strategies for clinical diagnosis. Finally, this paper discusses the challenges and future research directions faced by OD and OC segmentation when applied in the medical field to help readers comprehensively grasp the research dynamics in this area.

Dual Classifier Adaptation: Source-Free UDA via Adaptive Pseudo-Labels Learning

Different from Unsupervised Domain Adaptation (UDA), Source-Free Unsupervised Domain Adaptation (SFUDA) transfers source knowledge to target domain without accessing the source data, using only the source model, has attracted much attention recently. One mainstream SFUDA method fine-tunes the source model by self-training to generate pseudo-labels of the target data. However, due to the significant differences between different domains, these target pseudo-labels often contain some noise, and it will inevitably degenerates the target performance. For this purpose, we propose an innovative SFUDA method with adaptive pseudo-labels learning named Dual Classifier Adaptation (DCA). In DCA, a dual classifier structure is introduced to adaptively learn target pseudo-labels by cooperation between source and target classifiers. Simultaneously, the minimax entropy is introduced for target learning, in order to adapt target data to source model, while capture the intrinsic cluster structure in target domain as well. After compared our proposed method DCA with a range of UDA and SFUDA methods, DCA achieves far ahead performance on several benchmark datasets.

Unified multi-level neighbor clustering for Source-Free Unsupervised Domain Adaptation

Source-Free Unsupervised Domain Adaptation (SFUDA) transfers knowledge from the source domain to the target domain with using the source domain model instead of the source data, as the source data cannot be accessed in data privacy scenarios. Our method is based on a clustering assumption: although there is a domain shift, target data with similar semantic still form a cluster in the source feature space. We identify two levels of clustering. One is class-level neighbor clustering: data with the same label tend to form a large cluster. The other is instance-level neighbor clustering: data and its neighbors tend to share the same label. Previous methods only consider one level, and we consider that both are complementary. We propose a new SFUDA method called unified multi-level neighbor clustering to address class and instance consistency in a complementary way. Our proposed method achieves competitive results on three domain adaptation benchmark datasets.

Unsupervised Domain Adaptation for the Semantic Segmentation of Remote Sensing Images via a Class-aware Fourier Transform and a Fine-grained Discriminator

The semantic segmentation of remote sensing images is vital for Earth observation purposes. However, its performance can decline significantly due to differences in dataset distributions between training (source) and deployment (target) settings. Unsupervised domain adaptation can be used to counter this problem by leveraging the knowledge acquired from the labelled source domain and by adapting it to the unlabelled target domain. Existing methods focus on either input-level or feature-level alignments, which can be sub-optimal for addressing large domain gaps. To this end, this paper introduces a new unsupervised domain adaptation method that employs concurrently two levels of alignment: first, at the input level, an adaptive Fourier-based image-to-image translation approach is utilised to generate target-styled source images with class-based low-amplitude changes. Then, at the feature level, an adaptive fine-grained domain discriminator is introduced that incorporates class information into two parallel discriminators, for source vs. target and target-styled source image vs. target settings. Experimental results indicate that the proposed method improves significantly cross-domain semantic segmentation performance with respect to the state-of-the-art.

Multi-source domain adaptive network based on local kernelized higher-order moment matching for rotating machinery fault diagnosis

Unsupervised domain adaptation has been extensively researched in rotating-machinery cross-domain fault diagnosis. A multi-source domain adaptive network based on local kernelized higher-order moment matching is constructed in this research for rotating-machinery fault diagnosis. Firstly, a multi-branch network is designed to map each source-target pair to a domain-specific shared space and to extract domain-invariant features using domain adversarial thought. Then, a local kernelized higher-order moment matching algorithm is proposed to perform fine-grained matching in shared category subspace. Finally, a feature fusion strategy based on the local domain distribution deviation is applied to synthesize the output features of multiple classifiers to obtain diagnostic results. The experimental validation of two-branch and three-branch networks on two public datasets is carried out and average diagnostic accuracies both exceed 99%. The results demonstrate the effectiveness and superiority of the approach.

A Domain-Adaptive segmentation method based on segment Anything model for mechanical assembly

Semantic segmentation of assembly images can effectively identify and prevent non-standard behaviors such as wrong assembly and missing assembly in the assembly process. However, supervised methods require massive training samples and are time-consuming to label. Unsupervised domain adaptation methods can address this issue without labeling the target data. Recently, the large-scale Segment Anything Model (SAM) has achieved significant success in semantic segmentation owing to its powerful feature extraction and instant interaction capabilities. Based on this, this paper proposed a new framework called U-SAM for unsupervised domain-adaptive semantic segmentation. Compared to other frameworks, U-SAM can obtain more segmented features while maintaining shallower features. Meanwhile, this study proposed an iterative loop strategy for the prompt module in U-SAM. This strategy gradually improves the accuracy of target prompt labels and solves the problem of no prompt labels in the target data. Additionally, a novel method of using oblique boxes instead of regular boxes was developed to achieve higher box prompt quality. Finally, a line discriminator module and a trainable line-guided filtering module were designed to improve the quality of edge segmentation. The experimental results indicate that the U-SAM network framework achieves a Dice accuracy of 84.4% for semantic segmentation on the mechanical assembly image dataset. This confirmed that our proposed method can be applied to the assembly monitoring process of target data.

Remaining Useful Life Prediction Under Variable Operating Conditions via Multisource Adversarial Domain Adaptation Networks

Domain adaptation (DA) methods have been extensively applied for intelligent remaining useful life prediction (RUL) tasks, addressing domain shift issues under variable operating conditions. Almost all existing methods focus on exploiting the single-source unsupervised domain adaptation (SUDA) approach which only learns prognostic knowledge from a single domain. Nevertheless, SUDA methods have significant limitations due to the disregard for the valuable information contained in historical data from numerous sources. Compared to conventional SUDA approaches, multisource unsupervised domain adaptation (MUDA) approaches can acquire invariant domain representation from a variety of sources and offer higher generalization in transfer tasks. Therefore, a novel MUDA framework multisource adversarial domain adaptation network (MADAN) is proposed for regression tasks. MADAN can efficiently organize and utilize multiple source domain data through a two-stage adversarial domain alignment, of which adversarial domain distribution alignment and inter-domain regression alignment modules are designed to minimize domain discrepancy and regressor discrepancy between each pair of source and target domains. Experimental results indicate that MADAN achieves accurate cross-domain predictions by using multiple source domains, and a comparison demonstrates that it performs better than the state-of-the-art approaches.

Loose to compact feature alignment for domain adaptive object detection

Recently, great achievements have been made for deep learning based object detection methods. But their performance drops significantly when domain shifts occur. To address this problem, in this work we propose a loose to compact feature alignment method under an unsupervised domain adaptation framework. The entire feature alignment is performed in a divide and conquer manner, so as to distribute the alignment difficulties at two steps. At the first step, we loosen the goal at both image and instance levels. At the image level, a new Mask Guided Foreground Alignment (MGFA) module is proposed to make the alignment focus more on easier foreground regions, leaving the more diverse and more difficult background regions to the second step; at the instance level, we propose a Class-Wise Instance Alignment (CWIA) module with separated domain classifiers for different categories so as to ease the alignment. At the second step, the alignment is performed per pixel and per instance, achieving a more compact and better aligned feature space. We conduct experiments on three different adaptation scenarios, where we achieve comparable results, demonstrating the effectiveness of our proposed method.

DeNKD: Decoupled Non-Target Knowledge Distillation for Complementing Transformer-Based Unsupervised Domain Adaptation

DeNKD: Decoupled <i>Non-Target</i> Knowledge Distillation for Complementing Transformer-Based Unsupervised Domain Adaptation

A Copula Based Unsupervised Domain Adaptation for Image Classification

In this paper, we present an unsupervised domain adaptation algorithm for image classification using principal component analysis (PCA) and Gaussian copula function alignment. The motivation of the proposed algorithm stems from the idea of CORAL algorithm which extracts domain invariant features by aligning the correlation structure between a source and a target domain. However, it suffers from the fact that highly skewed marginal distributions happen to distort the correlation structure so that it may cause a negative transfer. Therefore we utilize a copula function that enables us to analyze separately the dependency structure and the marginals by Sklar’s theorem. In particular, we propose to align the Gaussian copula correlations in the copula feature spaces instead of aligning the correlation matrices in the original space. Considering the extremely skewed distribution of SURF image features in Office-Caltech10 data set we apply PCA first in order to extract some skewness-mitigated principal features and then derive copula features to align for domain adaptation by using CORAL idea with Gaussian copula correlation matrices. The proposed method showed a good classification accuracy when applied to image classification problem in an unsupervised domain adaptation setting.

Intelligent fault diagnosis under imbalanced multivariate working conditions leveraging dynamic unsupervised domain adaptation with sample and margin regularization

Utilizing unsupervised domain adaptation for intelligent fault diagnosis (IFD) has demonstrated significant potential for ensuring the security of machinery systems. Nonetheless, the inherent imbalance attribute of collected data affects the performance of diagnostic model. Especially, for machines working under varied conditions, the acquired unlabeled data frequently exhibits diverse degrees of distributional deviations, thus further undermining the transferable model’s generalization capability. To address this challenge, we introduce a method termed Dynamic Unsupervised Imbalanced Domain Adaptation (DUIDA) for IFD. Employment of class rebalancing and label-dependent margin regularization strategies optimizes the selection of decision boundaries which counteract the distributional deviations introduced by the imbalance. In addition, by integrating a dynamic weighting mechanism, encompassing both adversarial-based and MMD-based domain adaptation, our model becomes versatile across varied UIDA tasks, assigning higher weights to fundamental faulty features. Finally, our empirical analyses on two faulty bearing datasets substantiate the efficacy and superior performance of the proposed framework across diverse operational scenarios.

A New Method for Bearing Fault Diagnosis across Machines Based on Envelope Spectrum and Conditional Metric Learning.

In recent years, most research on bearing fault diagnosis has assumed that the source domain and target domain data come from the same machine. The differences in equipment lead to a decrease in diagnostic accuracy. To address this issue, unsupervised domain adaptation techniques have been introduced. However, most cross-device fault diagnosis models overlook the discriminative information under the marginal distribution, which restricts the performance of the models. In this paper, we propose a bearing fault diagnosis method based on envelope spectrum and conditional metric learning. First, envelope spectral analysis is used to extract frequency domain features. Then, to fully utilize the discriminative information from the label distribution, we construct a deep Siamese convolutional neural network based on conditional metric learning to eliminate the data distribution differences and extract common features from the source and target domain data. Finally, dynamic weighting factors are employed to improve the convergence performance of the model and optimize the training process. Experimental analysis is conducted on 12 cross-device tasks and compared with other relevant methods. The results show that the proposed method achieves the best performance on all three evaluation metrics.

Unsupervised domain adaptation by incremental learning for concept drifting data streams

Unsupervised domain adaptation by incremental learning for concept drifting data streams

Per-class curriculum for Unsupervised Domain Adaptation in semantic segmentation

AbstractAccurate training of deep neural networks for semantic segmentation requires a large number of pixel-level annotations of real images, which are expensive to generate or not even available. In this context, Unsupervised Domain Adaptation (UDA) can transfer knowledge from unlimited synthetic annotations to unlabeled real images of a given domain. UDA methods are composed of an initial training stage with labeled synthetic data followed by a second stage for feature alignment between labeled synthetic and unlabeled real data. In this paper, we propose a novel approach for UDA focusing the initial training stage, which leads to increased performance after adaptation. We introduce a curriculum strategy where each semantic class is learned progressively. Thereby, better features are obtained for the second stage. This curriculum is based on: (1) a class-scoring function to determine the difficulty of each semantic class, (2) a strategy for incremental learning based on scoring and pacing functions that limits the required training time unlike standard curriculum-based training and (3) a training loss to operate at class level. We extensively evaluate our approach as the first stage of several state-of-the-art UDA methods for semantic segmentation. Our results demonstrate significant performance enhancements across all methods: improvements of up to 10% for entropy-based techniques and 8% for adversarial methods. These findings underscore the dependency of UDA on the accuracy of the initial training. The implementation is available at https://github.com/vpulab/PCCL.

A Multi-Modal Egocentric Activity Recognition Approach towards Video Domain Generalization.

Egocentric activity recognition is a prominent computer vision task that is based on the use of wearable cameras. Since egocentric videos are captured through the perspective of the person wearing the camera, her/his body motions severely complicate the video content, imposing several challenges. In this work we propose a novel approach for domain-generalized egocentric human activity recognition. Typical approaches use a large amount of training data, aiming to cover all possible variants of each action. Moreover, several recent approaches have attempted to handle discrepancies between domains with a variety of costly and mostly unsupervised domain adaptation methods. In our approach we show that through simple manipulation of available source domain data and with minor involvement from the target domain, we are able to produce robust models, able to adequately predict human activity in egocentric video sequences. To this end, we introduce a novel three-stream deep neural network architecture combining elements of vision transformers and residual neural networks which are trained using multi-modal data. We evaluate the proposed approach using a challenging, egocentric video dataset and demonstrate its superiority over recent, state-of-the-art research works.

Transferable mapping shift network for unsupervised domain adaptation using in vibration signal fault diagnosis under variable conditions

Transferable mapping shift network for unsupervised domain adaptation using in vibration signal fault diagnosis under variable conditions

SFDA-CD: A Source-Free Unsupervised Domain Adaptation for VHR Image Change Detection

Deep models may have disappointing performance in real applications due to the domain shifts in data distributions between the source and target domain. Although a few unsupervised domain adaptation methods have been proposed to make the pre-train models effective on target domain datasets, constraints like data privacy, security, and transmission limits restrict access to VHR remote sensing images, making existing unsupervised domain adaptation methods almost ineffective in specific change detection areas. Therefore, we propose a source-free unsupervised domain adaptation change detection structure to complete specific change detection tasks, using only the pre-trained source model and unlabelled target data. The GAN-based source generation component is designed to generate synthetic source data, which, to some extent, reflects the distribution of the source domain. Moreover, these data can be utilised in model knowledge transfer. The model adaptation component facilitates knowledge transfer between models by minimising the differences between deep features, using AAM (Attention Adaptation Module) to extract the difference between high-level features, meanwhile we proposed ISM (Intra-domain Self-supervised Module) to train target model in a self-supervised strategy in order to improve the knowledge adaptation. Our SFDA-CD framework demonstrates superior accuracy over existing unsupervised domain adaptation change detection methods, which has 0.6% cIoU and 1.5% F1 score up in cross-regional tasks and 1.4% cIoU and 1.9% F1 score up in cross-scenario tasks, proving that it can effectively reduce the domain shift between the source and target domains even without access to source data. Additionally, it can facilitate knowledge transfer from the source model to the target model.