283 citations · 817 across the 58 of their papers we have counts for
13 papers · 1 filter
RID-Noise: Towards Robust Inverse Design under Noisy Environments
Jia-Qi Yang, Ke-Bin Fan, Hao Ma +1
From an engineering perspective, a design should not only perform well in an ideal condition, but should also resist noises. Such a design methodology, namely robust design, has be…
PyCIL: A Python Toolbox for Class-Incremental Learning
Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye +1
Traditional machine learning systems are deployed under the closed-world setting, which requires the entire training data before the offline training process. However, real-world a…
LINDA: Multi-Agent Local Information Decomposition for Awareness of Teammates
Jiahan Cao, Lei Yuan, Jianhao Wang +4
In cooperative multi-agent reinforcement learning (MARL), where agents only have access to partial observations, efficiently leveraging local information is critical. During long-t…
Co-Transport for Class-Incremental Learning
Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan
Traditional learning systems are trained in closed-world for a fixed number of classes, and need pre-collected datasets in advance. However, new classes often emerge in real-world…
Aggregate or Not? Exploring Where to Privatize in DNN Based Federated Learning Under Different Non-IID Scenes
Xin-Chun Li, Le Gan, De-Chuan Zhan +3
Although federated learning (FL) has recently been proposed for efficient distributed training and data privacy protection, it still encounters many obstacles. One of these is the…
Preliminary Steps Towards Federated Sentiment Classification
Xin-Chun Li, Lan Li, De-Chuan Zhan +3
Automatically mining sentiment tendency contained in natural language is a fundamental research to some artificial intelligent applications, where solutions alternate with challeng…
Few-Shot Learning with a Strong Teacher
Han-Jia Ye, Lu Ming, De-Chuan Zhan +1
Few-shot learning (FSL) aims to generate a classifier using limited labeled examples. Many existing works take the meta-learning approach, constructing a few-shot learner that can…
Contextualizing Meta-Learning via Learning to Decompose
Han-Jia Ye, Da-Wei Zhou, Lanqing Hong +3
Meta-learning has emerged as an efficient approach for constructing target models based on support sets. For example, the meta-learned embeddings enable the construction of target…
Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport
Yang Yang, Zhao-Yang Fu, De-Chuan Zhan +2
Complex objects are usually with multiple labels, and can be represented by multiple modal representations, e.g., the complex articles contain text and image information as well as…
Few-Shot Action Recognition with Compromised Metric via Optimal Transport
Su Lu, Han-Jia Ye, De-Chuan Zhan
Although vital to computer vision systems, few-shot action recognition is still not mature despite the wide research of few-shot image classification. Popular few-shot learning alg…
Towards Enabling Meta-Learning from Target Models
Su Lu, Han-Jia Ye, Le Gan +1
Meta-learning can extract an inductive bias from previous learning experience and assist the training of new tasks. It is often realized through optimizing a meta-model with the ev…
Procrustean Training for Imbalanced Deep Learning
Han-Jia Ye, De-Chuan Zhan, Wei-Lun Chao
Neural networks trained with class-imbalanced data are known to perform poorly on minor classes of scarce training data. Several recent works attribute this to over-fitting to mino…
Learning Placeholders for Open-Set Recognition
Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan
Traditional classifiers are deployed under closed-set setting, with both training and test classes belong to the same set. However, real-world applications probably face the input…