463 citations · 565 across the 20 of their papers we have counts for
8 papers · 1 filter
Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation
Ruihong Qiu, Zi Huang, Hongzhi Yin +1
Recent advancements of sequential deep learning models such as Transformer and BERT have significantly facilitated the sequential recommendation. However, according to our study, t…
Domain Adaptive Semantic Segmentation without Source Data
Fuming You, Jingjing Li, Lei Zhu +3
Domain adaptive semantic segmentation is recognized as a promising technique to alleviate the domain shift between the labeled source domain and the unlabeled target domain in many…
Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation
Ruihong Qiu, Zi Huang, Hongzhi Yin
The sequential recommendation aims to recommend items, such as products, songs and places, to users based on the sequential patterns of their historical records. Most existing sequ…
Learning to Diversify for Single Domain Generalization
Zijian Wang, Yadan Luo, Ruihong Qiu +2
Domain generalization (DG) aims to generalize a model trained on multiple source (i.e., training) domains to a distributionally different target (i.e., test) domain. In contrast to…
Exploiting Positional Information for Session-based Recommendation
Ruihong Qiu, Zi Huang, Tong Chen +1
For present e-commerce platforms, session-based recommender systems are developed to predict users' preference for next-item recommendation. Although a session can usually reflect…
Mitigating Generation Shifts for Generalized Zero-Shot Learning
Zhi Chen, Yadan Luo, Sen Wang +3
Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information (e.g., attributes) to recognize the seen and unseen samples, where unseen classes are not obser…
Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning
Zhi Chen, Zi Huang, Jingjing Li +1
Compared to conventional zero-shot learning (ZSL) where recognising unseen classes is the primary or only aim, the goal of generalized zero-shot learning (GZSL) is to recognise bot…
Semantics Disentangling for Generalized Zero-Shot Learning
Zhi Chen, Yadan Luo, Ruihong Qiu +4
Generalized zero-shot learning (GZSL) aims to classify samples under the assumption that some classes are not observable during training. To bridge the gap between the seen and uns…