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Found 5 papers, 2 papers with code
HierMUD: Hierarchical Multi-task Unsupervised Domain Adaptation between Bridges for Drive-by Damage Diagnosis
Monitoring bridge health using vibrations of drive-by vehicles has various benefits, such as no need for directly installing and maintaining sensors on the bridge.
Knowledge Transfer between Buildings for Seismic Damage Diagnosis through Adversarial Learning
The supervised learning requires historical structural response data and corresponding damage states (i. e., labels) for each building to learn the building-specific damage diagnosis model.
O-MedAL: Online Active Deep Learning for Medical Image Analysis
Our online method enhances performance of its underlying baseline deep network.
Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures
In this work, we give new results on the benefits of multi-generator architecture of GANs.
MedAL: Deep Active Learning Sampling Method for Medical Image Analysis
Active learning techniques can be used to minimize the number of required training labels while maximizing the model's performance. In this work, we propose a novel sampling method that queries the unlabeled examples that maximize the average distance to all training set examples in a learned feature space.
