Domain adaptation
View article
Return of Frustratingly Easy Domain Adaptation Open
Unlike human learning, machine learning often fails to handle changes between training (source) and test (target) input distributions. Such domain shifts, common in practical scenarios, severely damage the performance of conventional machi…
View article
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals Open
Intelligent fault diagnosis techniques have replaced time-consuming and unreliable human analysis, increasing the efficiency of fault diagnosis. Deep learning models can improve the accuracy of intelligent fault diagnosis with the help of …
View article
Conditional Adversarial Domain Adaptation Open
Adversarial learning has been embedded into deep networks to learn disentangled and transferable representations for domain adaptation. Existing adversarial domain adaptation methods may not effectively align different domains of multimoda…
View article
Multi-Task Deep Neural Networks for Natural Language Understanding Open
In this paper, we present a Multi-Task Deep Neural Network (MT-DNN) for learning representations across multiple natural language understanding (NLU) tasks. MT-DNN not only leverages large amounts of cross-task data, but also benefits from…
View article
Unsupervised Domain Adaptation with Residual Transfer Networks Open
The recent success of deep neural networks relies on massive amounts of labeled data. For a target task where labeled data is unavailable, domain adaptation can transfer a learner from a different source domain. In this paper, we propose a…
View article
Optimal Transport for Domain Adaptation Open
Domain adaptation is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data representation become more robust when confronted to data depicting the same classes, but…
View article
Multi-Adversarial Domain Adaptation Open
Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy between the source and target domains. Existing domain advers…
View article
Wasserstein Distance Guided Representation Learning for Domain Adaptation Open
Domain adaptation aims at generalizing a high-performance learner on a target domain via utilizing the knowledge distilled from a source domain which has a different but related data distribution. One solution to domain adaptation is to le…
View article
Visual Domain Adaptation with Manifold Embedded Distribution Alignment Open
Visual domain adaptation aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Existing methods either attempt to align the cross-domain distributions, or perform manifold subspace learning. H…
View article
CyCADA: Cycle-Consistent Adversarial Domain Adaptation Open
Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-le…
View article
VisDA: The Visual Domain Adaptation Challenge Open
We present the 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a large-scale testbed for unsupervised domain adaptation across visual domains. Unsupervised domain adaptation aims to solve the real-world problem of domain shift…
View article
Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes Open
During the last half decade, convolutional neural networks (CNNs) have\ntriumphed over semantic segmentation, which is one of the core tasks in many\napplications such as autonomous driving. However, to train CNNs requires a\nconsiderable …
View article
Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances Open
The success of supervised classification of remotely sensed images acquired\nover large geographical areas or at short time intervals strongly depends on\nthe representativity of the samples used to train the classification algorithm\nand …
View article
A Review of Domain Adaptation without Target Labels Open
Domain adaptation has become a prominent problem setting in machine learning and related fields. This review asks the question: How can a classifier learn from a source domain and generalize to a target domain? We present a categorization …
View article
Beyond Sharing Weights for Deep Domain Adaptation Open
The performance of a classifier trained on data coming from a specific domain typically degrades when applied to a related but different one. While annotating many samples from the new domain would address this issue, it is often too expen…
View article
Model Adaptation: Unsupervised Domain Adaptation Without Source Data Open
In this paper, we investigate a challenging unsupervised domain adaptation setting -- unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data to improve performance of an existing source prediction model …
View article
Deep multi-task learning with low level tasks supervised at lower layers Open
In all previous work on deep multi-task learning we are aware of, all task supervisions are on the same (outermost) layer. We present a multi-task learning architecture with deep bi-directional RNNs, where different tasks supervision can h…
View article
Asymmetric Tri-training for Unsupervised Domain Adaptation Open
Deep-layered models trained on a large number of labeled samples boost the accuracy of many tasks. It is important to apply such models to different domains because collecting many labeled samples in various domains is expensive. In unsupe…
View article
Transfer learning: a friendly introduction Open
Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the res…
View article
Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification Open
Person re-identification (re-ID) aims at identifying the same persons' images across different cameras. However, domain diversities between different datasets pose an evident challenge for adapting the re-ID model trained on one dataset to…
View article
Adversarial Domain Adaptation with Domain Mixup Open
Recent works on domain adaptation reveal the effectiveness of adversarial learning on filling the discrepancy between source and target domains. However, two common limitations exist in current adversarial-learning-based methods. First, sa…
View article
Deep CORAL: Correlation Alignment for Deep Domain Adaptation Open
Deep neural networks are able to learn powerful representations from large quantities of labeled input data, however they cannot always generalize well across changes in input distributions. Domain adaptation algorithms have been proposed …
View article
Revisiting Batch Normalization For Practical Domain Adaptation Open
Deep neural networks (DNN) have shown unprecedented success in various computer vision applications such as image classification and object detection. However, it is still a common annoyance during the training phase, that one has to prepa…
View article
Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources Open
While Unsupervised Domain Adaptation (UDA) algorithms, i.e., there are only labeled data from source domains, have been actively studied in recent years, most algorithms and theoretical results focus on Single-source Unsupervised Domain Ad…
View article
Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation Open
High-density surface electromyography (HD-sEMG) is to record muscles’ electrical activity from a restricted area of the skin by using two dimensional arrays of closely spaced electrodes. This technique allows the analysis and modelling of …
View article
Domain Adaptation Techniques for EEG-Based Emotion Recognition: A Comparative Study on Two Public Datasets Open
Affective brain-computer interface (aBCI) introduces personal affective factors to human-computer interaction. The state-of-the-art aBCI tailors its classifier to each individual user to achieve accurate emotion classification. A subject-i…
View article
Continual Test-Time Domain Adaptation Open
Test-time domain adaptation aims to adapt a source pretrained model to a target domain without using any source data. Existing works mainly consider the case where the target domain is static. However, real-world machine perception systems…
View article
A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference Open
This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. In addition to being one of the largest…
View article
Maximum Density Divergence for Domain Adaptation Open
Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptatio…
View article
Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss Open
Convolutional networks (ConvNets) have achieved great successes in various challenging vision tasks. However, the performance of ConvNets would degrade when encountering the domain shift. The domain adaptation is more significant while cha…