31 citations · 37 across the 6 of their papers we have counts for
6 papers
Semi-Supervised Learning under General Causal Models
Archer Moore, Heejung Shim, Jingge Zhu +1
Semi-supervised learning (SSL) aims to train a machine learning model using both labelled and unlabelled data. While the unlabelled data have been used in various ways to improve t…
Efficient Neural Implicit Representation for 3D Human Reconstruction
Zexu Huang, Sarah Monazam Erfani, Siying Lu +1
High-fidelity digital human representations are increasingly in demand in the digital world, particularly for interactive telepresence, AR/VR, 3D graphics, and the rapidly evolving…
Self-Distilled Disentangled Learning for Counterfactual Prediction
Xinshu Li, Mingming Gong, Lina Yao
The advancements in disentangled representation learning significantly enhance the accuracy of counterfactual predictions by granting precise control over instrumental variables, c…
An Efficient Transformer for Simultaneous Learning of BEV and Lane Representations in 3D Lane Detection
Ziye Chen, Kate Smith-Miles, Bo Du +2
Accurately detecting lane lines in 3D space is crucial for autonomous driving. Existing methods usually first transform image-view features into bird-eye-view (BEV) by aid of inver…
Robust Weight Perturbation for Adversarial Training
Chaojian Yu, Bo Han, Mingming Gong +4
Overfitting widely exists in adversarial robust training of deep networks. An effective remedy is adversarial weight perturbation, which injects the worst-case weight perturbation…
Instrumental Variable-Driven Domain Generalization with Unobserved Confounders
Junkun Yuan, Xu Ma, Ruoxuan Xiong +5
Domain generalization (DG) aims to learn from multiple source domains a model that can generalize well on unseen target domains. Existing DG methods mainly learn the representation…