1 citations · 3 across the 6 of their papers we have counts for
7 papers
Closing the Confusion Loop: CLIP-Guided Alignment for Source-Free Domain Adaptation
Shanshan Wang, Ziying Feng, Xiaozheng Shen +4
Source-Free Domain Adaptation (SFDA) tackles the problem of adapting a pre-trained source model to an unlabeled target domain without accessing any source data, which is quite suit…
Learning states enhanced knowledge tracing: Simulating the diversity in real-world learning process
Shanshan Wang, Xueying Zhang, Keyang Wang +2
The Knowledge Tracing (KT) task focuses on predicting a learner's future performance based on the historical interactions. The knowledge state plays a key role in learning process.…
Dual-stream Feature Augmentation for Domain Generalization
Shanshan Wang, ALuSi, Xun Yang +3
Domain generalization (DG) task aims to learn a robust model from source domains that could handle the out-of-distribution (OOD) issue. In order to improve the generalization abili…
Gradually Vanishing Gap in Prototypical Network for Unsupervised Domain Adaptation
Shanshan Wang, Hao Zhou, Xun Yang +4
Unsupervised domain adaptation (UDA) is a critical problem for transfer learning, which aims to transfer the semantic information from labeled source domain to unlabeled target dom…
Dual-State Personalized Knowledge Tracing with Emotional Incorporation
Shanshan Wang, Fangzheng Yuan, Keyang Wang +3
Knowledge tracing has been widely used in online learning systems to guide the students' future learning. However, most existing KT models primarily focus on extracting abundant in…
Personalized Forgetting Mechanism with Concept-Driven Knowledge Tracing
Shanshan Wang, Ying Hu, Xun Yang +3
Knowledge Tracing (KT) aims to trace changes in students' knowledge states throughout their entire learning process by analyzing their historical learning data and predicting their…
BP-Triplet Net for Unsupervised Domain Adaptation: A Bayesian Perspective
Shanshan Wang, Lei Zhang, Pichao Wang
Triplet loss, one of the deep metric learning (DML) methods, is to learn the embeddings where examples from the same class are closer than examples from different classes. Motivate…