1k citations · 1.6k across the 21 of their papers we have counts for
21 papers
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…
ALL-E: Aesthetics-guided Low-light Image Enhancement
Ling Li, Dong Liang, Yuanhang Gao +2
Evaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into image enhancement a necessity. Existing methods…
MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection
Dong Liang, Jing-Wei Zhang, Ying-Peng Tang +1
Recent aerial object detection models rely on a large amount of labeled training data, which requires unaffordable manual labeling costs in large aerial scenes with dense objects.…
A Deep Model for Partial Multi-Label Image Classification with Curriculum Based Disambiguation
Feng Sun, Ming-Kun Xie, Sheng-Jun Huang
In this paper, we study the partial multi-label (PML) image classification problem, where each image is annotated with a candidate label set consists of multiple relevant labels an…
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…
Oscars: Adaptive Semi-Synchronous Parallel Model for Distributed Deep Learning with Global View
Sheng Huang
Deep learning has become an indispensable part of life, such as face recognition, NLP, etc., but the training of deep model has always been a challenge, and in recent years, the co…
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…
WoCE: a framework for clustering ensemble by exploiting the wisdom of Crowds theory
Muhammad Yousefnezhad, Sheng-Jun Huang, Daoqiang Zhang
The Wisdom of Crowds (WOC), as a theory in the social science, gets a new paradigm in computer science. The WOC theory explains that the aggregate decision made by a group is often…
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…