102 citations · 188 across the 8 of their papers we have counts for
10 papers
DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices
Ismail Nejjar, Qin Wang, Olga Fink
Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems. Recent work…
Multi-agent Actor-Critic with Time Dynamical Opponent Model
Yuan Tian, Klaus-Rudolf Kladny, Qin Wang +2
In multi-agent reinforcement learning, multiple agents learn simultaneously while interacting with a common environment and each other. Since the agents adapt their policies during…
Continual Test-Time Domain Adaptation
Qin Wang, Olga Fink, Luc Van Gool +1
Test-time domain adaptation aims to adapt a source pre-trained model to a target domain without using any source data. Existing works mainly consider the case where the target doma…
Integrating Expert Knowledge with Domain Adaptation for Unsupervised Fault Diagnosis
Qin Wang, Cees Taal, Olga Fink
Data-driven fault diagnosis methods often require abundant labeled examples for each fault type. On the contrary, real-world data is often unlabeled and consists of mostly healthy…
Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
Qin Wang, Dengxin Dai, Lukas Hoyer +2
Domain adaptation for semantic segmentation aims to improve the model performance in the presence of a distribution shift between source and target domain. Leveraging the supervisi…
Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search
Yuan Tian, Qin Wang, Zhiwu Huang +5
In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) ar…
Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications
Olga Fink, Qin Wang, Markus Svensén +3
Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding. The drivers for the vibr…
Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis
Qin Wang, Gabriel Michau, Olga Fink
Domain adaptation aims at improving model performance by leveraging the learned knowledge in the source domain and transferring it to the target domain. Recently, domain adversaria…
Substation Signal Matching with a Bagged Token Classifier
Qin Wang, Sandro Schoenborn, Yvonne-Anne Pignolet +2
Currently, engineers at substation service providers match customer data with the corresponding internally used signal names manually. This paper proposes a machine learning method…
ContextVP: Fully Context-Aware Video Prediction
Wonmin Byeon, Qin Wang, Rupesh Kumar Srivastava +1
Video prediction models based on convolutional networks, recurrent networks, and their combinations often result in blurry predictions. We identify an important contributing factor…