486 citations · 2.3k across the 55 of their papers we have counts for
8 papers · 1 filter
Few-shot Learning with Global Relatedness Decoupled-Distillation
Yuan Zhou, Yanrong Guo, Shijie Hao +3
Despite the success that metric learning based approaches have achieved in few-shot learning, recent works reveal the ineffectiveness of their episodic training mode. In this paper…
Privileged Graph Distillation for Cold Start Recommendation
Shuai Wang, Kun Zhang, Le Wu +3
The cold start problem in recommender systems is a long-standing challenge, which requires recommending to new users (items) based on attributes without any historical interaction…
Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation
Lei Chen, Le Wu, Kun Zhang +2
As users often express their preferences with binary behavior data~(implicit feedback), such as clicking items or buying products, implicit feedback based Collaborative Filtering~(…
Few-shot Partial Multi-view Learning
Yuan Zhou, Yanrong Guo, Shijie Hao +2
It is often the case that data are with multiple views in real-world applications. Fully exploring the information of each view is significant for making data more representative.…
Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning
Jianfeng Dong, Zhe Ma, Xiaofeng Mao +4
This paper strives to predict fine-grained fashion similarity. In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute…
Revisiting Local Descriptor for Improved Few-Shot Classification
Jun He, Richang Hong, Xueliang Liu +2
Few-shot classification studies the problem of quickly adapting a deep learner to understanding novel classes based on few support images. In this context, recent research efforts…
Learning Fair Representations for Recommendation: A Graph-based Perspective
Le Wu, Lei Chen, Pengyang Shao +3
As a key application of artificial intelligence, recommender systems are among the most pervasive computer aided systems to help users find potential items of interests. Recently,…
Deep Adversarial Inconsistent Cognitive Sampling for Multi-view Progressive Subspace Clustering
Renhao Sun
Deep multi-view clustering methods have achieved remarkable performance. However, all of them failed to consider the difficulty labels (uncertainty of ground-truth for training sam…