Zero-Shot Domain Adaptation

Latest papers

8 papers1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Cross-Asset Knowledge Transfer for Early Fault Detection of Data-Scarce Assets Without Target Fault Data
Nov 4, 2026Zekai Wang, Yuxuan Li, Zhangyue ShiZero-Shot Domain Adaptation Fault Detection
Charles F. Dolan School of Business, Fairfield University, Fairfield, CT, United States · School of Economics and Management, Tiangong University, Tianjin, China · Independent Researcher, Chesterfield, MO, United States
Enhancing Zero-Shot Domain Transfer in Low-Resource Sentiment Analysis: A Case Study in Marathi
Oct 11, 2026Tanvi Somani, Aryan Babare, Samyak Bora +1Zero-Shot Domain Adaptation Sentiment Analysis
Pune Institute of Computer Technology (PICT), Pune, Maharashtra, India · L3Cube Labs, Pune · Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
Relative Transitions, Not Absolute Destinations: A Transfer-and-Ground Framework for Target-Trajectory-Free Human Mobility Generation
Oct 1, 2026Yidi Wang, Yunhe Zhang, Bangchao Deng +2Zero-Shot Domain Adaptation Trajectory Generation
SKL-IOTSC, Department of CIS, University of Macau Macau SAR, China
Domain-adaptive Zero-Shot Image Enhancement via Locality-Constrained Diffusion Guidance
Sep 28, 2026Theresa Neubauer, Dimitrios Lenis, Astrid Berg +7Diffusion Model Guidance Zero-Shot Domain Adaptation
VRVis GmbH, Vienna, Austria
An Adversarial Zero-Shot Learning Approach for Anomaly Detection in Multivariate IoT Traffic Data
Sep 3, 2026Mahshid Rezakhani, Tolunay Seyfi, Fatemeh AfghahDomain-Adversarial Learning Zero-Shot Domain Adaptation
Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, 29634 SC, USA
Large-Small Model Collaboration for Zero-Shot Surgical Phase Recognition
Aug 24, 2026Yiyi Zhang, Ying Zheng, Wenxin Fan +5Zero-Shot Domain Adaptation Surgical Video Understanding
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China · Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, China · Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China +1
LELA: An End-to-end LLM-based Entity Linking Framework with Zero-shot Domain Adaptation
May 26, 2026Samy Haffoudhi, Nikola Dobričić, Fabian Suchanek +1Zero-Shot Domain Adaptation LLM Information Extraction
IP Paris, LTCI, DIG · T´el´ecom Paris, Institut Polytechnique de Paris · IP Paris +1
SIDA: Synthetic Image Driven Zero-shot Domain Adaptation
Jul 24, 2025Ye-Chan Kim, SeungJu Cha, Si-Woo Kim +2Zero-Shot Domain Adaptation
Hanyang University

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Cross-Asset Knowledge Transfer for Early Fault Detection of Data-Scarce Assets Without Target Fault Data

Zekai Wang, Yuxuan Li, Zhangyue Shi

Charles F. Dolan School of Business, Fairfield University, Fairfield, CT, United States · School of Economics and Management, Tiangong University, Tianjin, China · Independent Researcher, Chesterfield, MO, United States

Zero-Shot Domain Adaptation Fault Detection Industrial Fault Diagnosis Domain-Adversarial Learning Bearing Fault Diagnosis

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Reliability management of industrial fleets depends on early fault warnings, yet lifecycle data across similar assets are highly imbalanced. A few assets accumulate run-to-failure histories, while newly installed or rarely failing assets provide only normal-condition data. This paper asks how diagnostic knowledge can be transferred from a data-rich source asset to a data-scarce target asset when no fault data have been observed on the target. We formulate cross-asset early fault detection as a zero-shot domain adaptation problem, in which zero-shot refers to the absence of target fault samples, and propose a framework that requires only normal data from the target. The framework comprises (1) a statistical process control step that labels the source asset run-to-failure data with an exponentially weighted moving average chart, reducing manual annotation to verifying flagged segments, (2) an Adapted Domain-Adversarial Neural Network (ADANN) that combines domain-adversarial training and a Maximum Mean Discrepancy penalty with an Adapted Classification-Domain (ACD) loss, which anchors the classifier normal region to the target own normal data, and (3) an ahead-of-time labeling scheme with a prediction horizon that targets incipient faults. Under an exact-classification assumption, a theoretical analysis characterizes the domain-adversarial term for a normal-only target and the alignment state in which source fault features remain separated from target normal features. On a public run-to-failure bearing dataset, ADANN attains the highest mean F1-score among four transfer baselines, with a nominally significant precision gain over the strongest baseline, and its robustness is examined under synthetic noise.