33 citations · 54 across the 12 of their papers we have counts for
6 papers · 1 filter
Backdoor Cleansing with Unlabeled Data
Lu Pang, Tao Sun, Haibin Ling +1
Due to the increasing computational demand of Deep Neural Networks (DNNs), companies and organizations have begun to outsource the training process. However, the externally trained…
Local Context-Aware Active Domain Adaptation
Tao Sun, Cheng Lu, Haibin Ling
Active Domain Adaptation (ADA) queries the labels of a small number of selected target samples to help adapting a model from a source domain to a target domain. The local context o…
Domain Adaptation with Adversarial Training on Penultimate Activations
Tao Sun, Cheng Lu, Haibin Ling
Enhancing model prediction confidence on target data is an important objective in Unsupervised Domain Adaptation (UDA). In this paper, we explore adversarial training on penultimat…
Prior Knowledge Guided Unsupervised Domain Adaptation
Tao Sun, Cheng Lu, Haibin Ling
The waive of labels in the target domain makes Unsupervised Domain Adaptation (UDA) an attractive technique in many real-world applications, though it also brings great challenges…
Incorporation of density scaling constraint in density functional design via contrastive representation learning
Weiyi Gong, Tao Sun, Hexin Bai +7
In a data-driven paradigm, machine learning (ML) is the central component for developing accurate and universal exchange-correlation (XC) functionals in density functional theory (…
Safe Self-Refinement for Transformer-based Domain Adaptation
Tao Sun, Cheng Lu, Tianshuo Zhang +1
Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source domain to solve tasks on a related unlabeled target domain. It is a challenging problem especially when a…