Multi-Label Metric Transfer Learning Jointly Considering Instance Space and Label Space Distribution Divergence

Jiang, Siyu; Xu, Yonghui; Wang, Tengyun; Yang, Haizhi; Qiu, Shaojian; Yu, Han; Song, Hengjie

doi:10.1109/access.2018.2889572

Cited by 16 publications

(7 citation statements)

References 30 publications

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“…Unsupervised domain adaptation assumes that only unlabeled data are available in the target domain. The core idea is to find a shared feature space which can reduce the domain distribution divergence (Lee et al 2007;Qiu et al 2017;Wang and Mahadevan 2008;Jiang et al 2019;Feng, Yu, and Duarte 2020). Pan et al (Pan et al 2009) and Long et al (Long et al 2015) proposed to minimize the Maximum Mean Discrepancies (MMD) to align the domain distributions.…”

Section: Application Descriptionmentioning

confidence: 99%

Prior-Guided Transfer Learning for Enhancing Item Representation in E-commerce

Zeng

et al. 2022

AAAI

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Item representation learning is crucial for search and recommendation tasks in e-commerce. In e-commerce, the instances (e.g., items, users) in different domains are always related. Such instance relationship across domains contains useful local information for transfer learning. However, existing transfer learning based approaches did not leverage this knowledge. In this paper, we report on our experience designing and deploying Prior-Guided Transfer Learning (PGTL) to bridge this gap. It utilizes the instance relationship across domains to extract prior knowledge for the target domain and leverages it to guide the fine-grained transfer learning for e-commerce item representation learning tasks. Rather than directly transferring knowledge from the source domain to the target domain, the prior knowledge can serve as a bridge to link both domains and enhance knowledge transfer, especially when the domain distribution discrepancy is large. Since its deployment on the Taiwanese portal of Taobao in Aug 2020, PGTL has significantly improved the item exposure rate and item click-through rate compared to previous approaches

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Section: Application Descriptionmentioning

confidence: 99%

Prior-Guided Transfer Learning for Enhancing Item Representation in E-commerce

Zeng

et al. 2022

AAAI

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“…For the problem of heterogeneous unsupervised domain adaptation, a shared fuzzy equivalence relation (SFER) method was proposed [39]. Based on distribution adaptation, Jiang et al [40] proposed a multi-label metric transfer learning (MLMTL). Recently, Tan et al [41] studied a novel transfer learning problem termed Distant Domain Transfer Learning (DDTL).…”

Section: B Multi-task Transfer Learningmentioning

confidence: 99%

2M BeautyNet: Facial Beauty Prediction Based on Multi-Task Transfer Learning

Gan

Scotti

et al. 2020

IEEE Access

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Facial beauty prediction (FBP) has become an emerging area in the field of artificial intelligence. However, the lacks of data and accurate face representation hinder the development of FBP. Multi-task transfer learning can effectively avoid over-fitting, and utilize auxiliary information of related tasks to optimize the main task. In this paper, we present a network named Multi-input Multi-task Beauty Network (2M BeautyNet) and use transfer learning to predict facial beauty. In the experiment, beauty prediction is the main task, and gender recognition is the auxiliary. For multi-task training, we employ multitask loss weights automatic learning strategy to improve the performance of FBP. Finally, we replace the softmax classifier with a random forest. We conduct experiments on the Large Scale Facial Beauty Database (LSFBD) and SCUT-FBP5500 database. Results show that our method has achieved good results on LSFBD, the accuracy of FBP is up to 68.23%. Our 2M BeautyNet structure is suitable for multiple inputs of different databases. INDEX TERMS Facial beauty prediction, multi-task transfer learning, multi-input multi-output network, multi-task loss weight automatic learning strategy.

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“…e results of human motion posture tracking can be used as a reference for motion evaluation and objectively and accurately re ect the motion exibility of human, and they can also be used as a diagnostic reference for clinical medicine [1]. With the rapid development of arti cial intelligence technology, human motion posture tracking technology is applied more widely, and three-dimensional (3D) human motion tracking technology appears.…”

Section: Introductionmentioning

confidence: 99%

3D Human Motion Posture Tracking Method Using Multilabel Transfer Learning

Zhu

Liu

2022

Mobile Information Systems

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To overcome the high position and posture angle tracking error, long tracking loss time and posture tracking update response time, and low fitness problem of traditional human motion posture tracking methods, in this paper, a three-dimensional (3D) human motion posture tracking method using multilabel transfer learning is proposed. According to the human structure composition and degree of freedom constraints, the 3D human joint skeleton model is constructed to generate the 3D human pose image and perform the noise reduction operation. The background difference is used to detect the 3D human moving target. Using multilabel transfer learning, human motion posture features are extracted from joint position and joint angle, and the estimation results of 3D human motion posture are obtained. The tracking error of human motion posture is corrected by three-step search, and the visual 3D human motion posture tracking results are output. The results show that, compared with the traditional human motion posture tracking method, the position and posture angle tracking errors of the proposed method are 2.18 mm and 0.178 deg, respectively. The tracking loss time and posture tracking update response time are shorter, which proves that the proposed method has more advantages in tracking accuracy and higher adaptability.

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Multi-Label Metric Transfer Learning Jointly Considering Instance Space and Label Space Distribution Divergence

Cited by 16 publications

References 30 publications

Prior-Guided Transfer Learning for Enhancing Item Representation in E-commerce

Prior-Guided Transfer Learning for Enhancing Item Representation in E-commerce

2M BeautyNet: Facial Beauty Prediction Based on Multi-Task Transfer Learning

3D Human Motion Posture Tracking Method Using Multilabel Transfer Learning

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