1k citations · 1.6k across the 21 of their papers we have counts for
16 papers · 1 filter
Multi-Label Knowledge Distillation
Penghui Yang, Ming-Kun Xie, Chen-Chen Zong +4
Existing knowledge distillation methods typically work by imparting the knowledge of output logits or intermediate feature maps from the teacher network to the student network, whi…
Class-Distribution-Aware Pseudo Labeling for Semi-Supervised Multi-Label Learning
Ming-Kun Xie, Jia-Hao Xiao, Hao-Zhe Liu +3
Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data. However, in the context of semi-supervised multi-label learning (SSMLL), conventional…
Active Learning for Open-set Annotation
Kun-Peng Ning, Xun Zhao, Yu Li +1
Existing active learning studies typically work in the closed-set setting by assuming that all data examples to be labeled are drawn from known classes. However, in real annotation…
Can Adversarial Training Be Manipulated By Non-Robust Features?
Lue Tao, Lei Feng, Hongxin Wei +3
Adversarial training, originally designed to resist test-time adversarial examples, has shown to be promising in mitigating training-time availability attacks. This defense ability…
Learning from Crowds with Sparse and Imbalanced Annotations
Ye Shi, Shao-Yuan Li, Sheng-Jun Huang
Traditional supervised learning requires ground truth labels for the training data, whose collection can be difficult in many cases. Recently, crowdsourcing has established itself…
CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise
Ming-Kun Xie, Sheng-Jun Huang
Class-conditional noise commonly exists in machine learning tasks, where the class label is corrupted with a probability depending on its ground-truth. Many research efforts have b…
Gym-RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning
Shengyi Huang, Santiago Ontañón, Chris Bamford +1
In recent years, researchers have achieved great success in applying Deep Reinforcement Learning (DRL) algorithms to Real-time Strategy (RTS) games, creating strong autonomous agen…
Improving Model Robustness by Adaptively Correcting Perturbation Levels with Active Queries
Kun-Peng Ning, Lue Tao, Songcan Chen +1
In addition to high accuracy, robustness is becoming increasingly important for machine learning models in various applications. Recently, much research has been devoted to improvi…
Co-Imitation Learning without Expert Demonstration
Kun-Peng Ning, Hu Xu, Kun Zhu +1
Imitation learning is a primary approach to improve the efficiency of reinforcement learning by exploiting the expert demonstrations. However, in many real scenarios, obtaining exp…
Reinforcement Learning with Supervision from Noisy Demonstrations
Kun-Peng Ning, Sheng-Jun Huang
Reinforcement learning has achieved great success in various applications. To learn an effective policy for the agent, it usually requires a huge amount of data by interacting with…
ALiPy: Active Learning in Python
Ying-Peng Tang, Guo-Xiang Li, Sheng-Jun Huang
Supervised machine learning methods usually require a large set of labeled examples for model training. However, in many real applications, there are plentiful unlabeled data but l…
Recent Advances in Open Set Recognition: A Survey
Chuanxing Geng, Sheng-jun Huang, Songcan Chen
In real-world recognition/classification tasks, limited by various objective factors, it is usually difficult to collect training samples to exhaust all classes when training a rec…
Cost-Effective Training of Deep CNNs with Active Model Adaptation
Sheng-Jun Huang, Jia-Wei Zhao, Zhao-Yang Liu
Deep convolutional neural networks have achieved great success in various applications. However, training an effective DNN model for a specific task is rather challenging because i…
Active Feature Acquisition with Supervised Matrix Completion
Sheng-Jun Huang, Miao Xu, Ming-Kun Xie +3
Feature missing is a serious problem in many applications, which may lead to low quality of training data and further significantly degrade the learning performance. While feature…
Fast Multi-Instance Multi-Label Learning
Sheng-Jun Huang, Zhi-Hua Zhou
In many real-world tasks, particularly those involving data objects with complicated semantics such as images and texts, one object can be represented by multiple instances and sim…
Multi-Instance Multi-Label Learning
Zhi-Hua Zhou, Min-Ling Zhang, Sheng-Jun Huang +1
In this paper, we propose the MIML (Multi-Instance Multi-Label learning) framework where an example is described by multiple instances and associated with multiple class labels. Co…