Data-Dependent Label Distribution Learning for Age Estimation

2017 ◽  
Vol 26 (8) ◽  
pp. 3846-3858 ◽  
Author(s):  
Zhouzhou He ◽  
Xi Li ◽  
Zhongfei Zhang ◽  
Fei Wu ◽  
Xin Geng ◽  
...  
Keyword(s):  
Age Estimation ◽  
Label Distribution Learning ◽  
Label Distribution

scholarly journals Label Enhancement for Label Distribution Learning via Prior Knowledge

2020 ◽  
Author(s):  
Yongbiao Gao ◽  
Yu Zhang ◽  
Xin Geng
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Emotion Recognition ◽  
Prior Knowledge ◽  
Decision Process ◽  
Age Estimation ◽  
State Of The Art ◽  
Learning Agent ◽  
Label Distribution Learning ◽  
Label Distribution

Label distribution learning (LDL) is a novel machine learning paradigm that gives a description degree of each label to an instance. However, most of training datasets only contain simple logical labels rather than label distributions due to the difficulty of obtaining the label distributions directly. We propose to use the prior knowledge to recover the label distributions. The process of recovering the label distributions from the logical labels is called label enhancement. In this paper, we formulate the label enhancement as a dynamic decision process. Thus, the label distribution is adjusted by a series of actions conducted by a reinforcement learning agent according to sequential state representations. The target state is defined by the prior knowledge. Experimental results show that the proposed approach outperforms the state-of-the-art methods in both age estimation and image emotion recognition.

scholarly journals Age Label Distribution Learning Based on Unsupervised Comparisons of Faces

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Qiyuan Li ◽  
Zongyong Deng ◽  
Weichang Xu ◽  
Zhendong Li ◽  
Hao Liu
Keyword(s):  
Age Estimation ◽  
State Of The Art ◽  
Processing Method ◽  
Wireless Sensor ◽  
Learning Method ◽  
Significant Progress ◽  
Meaningful Information ◽  
Label Distribution Learning ◽  
Label Distribution ◽  
Facial Age

Although label distribution learning has made significant progress in the field of face age estimation, unsupervised learning has not been widely adopted and is still an important and challenging task. In this work, we propose an unsupervised contrastive label distribution learning method (UCLD) for facial age estimation. This method is helpful to extract semantic and meaningful information of raw faces with preserving high-order correlation between adjacent ages. Similar to the processing method of wireless sensor network, we designed the ConAge network with the contrast learning method. As a result, our model maximizes the similarity of positive samples by data enhancement and simultaneously pushes the clusters of negative samples apart. Compared to state-of-the-art methods, we achieve compelling results on the widely used benchmark, i.e., MORPH.

Active label distribution learning

2021 ◽  
Vol 436 ◽  
pp. 12-21
Author(s):  
Xinyue Dong ◽  
Shilin Gu ◽  
Wenzhang Zhuge ◽  
Tingjin Luo ◽  
Chenping Hou
Keyword(s):  
Label Distribution Learning ◽  
Label Distribution

Label distribution learning by maintaining label ranking relation

2021 ◽  
pp. 1-1
Author(s):  
Xiuyi Jia ◽  
Xiaoxia Shen ◽  
Weiwei Li ◽  
Yunan Lu ◽  
Jihua Zhu
Keyword(s):  
Label Ranking ◽  
Label Distribution Learning ◽  
Label Distribution
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