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Maximizing conditional independence for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) studies how to transfer a learner from a labeled source domain to an unlabeled target domain with different...
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Entropy minimization and domain adversarial training guided by label distribution similarity for domain adaptation
In domain adaptation, entropy minimization is widely used. However, entropy minimization will bring negative transfer when the pseudo-labels are...
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Adaptive prototype and consistency alignment for semi-supervised domain adaptation
Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a label-rich source domain to an unlabeled target domain whose data...
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Language-Aware Soft Prompting: Text-to-Text Optimization for Few- and Zero-Shot Adaptation of V &L Models
Soft prompt learning has emerged as a promising direction for adapting V &L models to a downstream task using a few training examples. However,...
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An encoding-aware bitrate adaptation mechanism for video streaming over HTTP
The great interest in flix-like services has amplified multimedia traffic over the Internet. Recently released traffic forecasting predicts that...
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Unsupervised domain adaptation via transferred local Fisher discriminant analysis
Domain adaptation in machine learning and image processing aims to benefit from gained knowledge of the multiple labeled training sets (i.e. source...
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A novel class-level weighted partial domain adaptation network for defect detection
Recently, unsupervised domain adaptation methods have been increasingly applied to address the domain shift problems in defect detection. However,...
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Mining Label Distribution Drift in Unsupervised Domain Adaptation
Unsupervised domain adaptation targets to transfer task-related knowledge from labeled source domain to unlabeled target domain. Although tremendous...
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NaCL: noise-robust cross-domain contrastive learning for unsupervised domain adaptation
The Unsupervised Domain Adaptation (UDA) methods aim to enhance feature transferability possibly at the expense of feature discriminability....
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Metal artifact correction in head computed tomography based on a homographic adaptation convolution neural network
In dental treatment, an increasing number of patients choose metal-implant surgery to treat oral conditions. Computed tomography (CT) images of...
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Exploiting Inter-Sample Affinity for Knowability-Aware Universal Domain Adaptation
Universal domain adaptation aims to transfer the knowledge of common classes from the source domain to the target domain without any prior knowledge...
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MixStyle Neural Networks for Domain Generalization and Adaptation
Neural networks do not generalize well to unseen data with domain shifts—a longstanding problem in machine learning and AI. To overcome the problem,...
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Adversarial domain adaptation for cross-project defect prediction
Cross-Project Defect Prediction (CPDP) is an attractive topic for locating defects in projects with little labeled data (target projects) by using...
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Uncertainty-Guided Source-Free Domain Adaptation
Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. However,...
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Decomposed-distance weighted optimal transport for unsupervised domain adaptation
Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a label-rich source domain to an unlabeled target domain with a different but...
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Domain Adaptation with Maximum Margin Criterion with Application to Network Traffic Classification
A fundamental assumption in machine learning is that training and test samples follow the same distribution. Therefore, for training a machine...
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MADAN: Multi-source Adversarial Domain Aggregation Network for Domain Adaptation
Domain adaptation aims to learn a transferable model to bridge the domain shift between one labeled source domain and another sparsely labeled or...
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Self-Training with Label-Feature-Consistency for Domain Adaptation
Mainstream approaches for unsupervised domain adaptation (UDA) learn domain-invariant representations to address the domain shift. Recently,...
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Multiple-Source Adaptation Using Variational Rényi Bound Optimization
Multiple Source Adaptation (MSA) is a problem that involves identifying a predictor which minimizes the error for the target domain while utilizing...
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Art in the Machine: Value Misalignment and AI “Art”
Why have online artist communities largely rejected AI image generators when they have embraced other technologies? We focus on cooperative design...