6 citations · 11 across the 9 of their papers we have counts for
9 papers
Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation
Jiajin Zhang, Hanqing Chao, Amit Dhurandhar +4
Domain shift is a common problem in clinical applications, where the training images (source domain) and the test images (target domain) are under different distributions. Unsuperv…
Dynamic Ensemble of Low-fidelity Experts: Mitigating NAS "Cold-Start"
Junbo Zhao, Xuefei Ning, Enshu Liu +7
Predictor-based Neural Architecture Search (NAS) employs an architecture performance predictor to improve the sample efficiency. However, predictor-based NAS suffers from the sever…
When Neural Networks Fail to Generalize? A Model Sensitivity Perspective
Jiajin Zhang, Hanqing Chao, Amit Dhurandhar +4
Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions. This paper considers a more realistic yet more challenging scenari…
Model-based Reinforcement Learning with Multi-step Plan Value Estimation
Haoxin Lin, Yihao Sun, Jiaji Zhang +1
A promising way to improve the sample efficiency of reinforcement learning is model-based methods, in which many explorations and evaluations can happen in the learned models to sa…
Regression Metric Loss: Learning a Semantic Representation Space for Medical Images
Hanqing Chao, Jiajin Zhang, Pingkun Yan
Regression plays an essential role in many medical imaging applications for estimating various clinical risk or measurement scores. While training strategies and loss functions hav…
Task-Oriented Low-Dose CT Image Denoising
Jiajin Zhang, Hanqing Chao, Xuanang Xu +3
The extensive use of medical CT has raised a public concern over the radiation dose to the patient. Reducing the radiation dose leads to increased CT image noise and artifacts, whi…
VEGA: Towards an End-to-End Configurable AutoML Pipeline
Bochao Wang, Hang Xu, Jiajin Zhang +21
Automated Machine Learning (AutoML) is an important industrial solution for automatic discovery and deployment of the machine learning models. However, designing an integrated Auto…
Robustified Domain Adaptation
Jiajin Zhang, Hanqing Chao, Pingkun Yan
Unsupervised domain adaptation (UDA) is widely used to transfer knowledge from a labeled source domain to an unlabeled target domain with different data distribution. While extensi…
Difference-in-Differences: Bridging Normalization and Disentanglement in PG-GAN
Xiao Liu, Jiajie Zhang, Siting Li +2
What mechanisms causes GAN's entanglement? Although developing disentangled GAN has attracted sufficient attention, it is unclear how entanglement is originated by GAN transformati…