scholarly journals Fashion Style Classification by Weakly Supervised Learning Using Large-Scale Clothing Image Database

2021 ◽  
Vol 87 (1) ◽  
pp. 92-98
Author(s):  
Munetaka MINOGUCHI ◽  
Kai WATABE ◽  
Ryosuke YAMADA ◽  
Hirokatsu KATAOKA ◽  
Akio NAKAMURA
Keyword(s):  
Supervised Learning ◽  
Large Scale ◽  
Image Database ◽  
Weakly Supervised Learning ◽  
Weakly Supervised

scholarly journals CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images

2018 ◽  
pp. 139-154 ◽  
Author(s):  
Sheng Guo ◽  
Weilin Huang ◽  
Haozhi Zhang ◽  
Chenfan Zhuang ◽  
Dengke Dong ◽  
...  
Keyword(s):  
Supervised Learning ◽  
Large Scale ◽  
Weakly Supervised Learning ◽  
Web Images ◽  
Weakly Supervised

scholarly journals IWE-Net: Instance Weight Network for Locating Negative Comments and its application to improve Traffic User Experience

2020 ◽  
Vol 34 (04) ◽  
pp. 4052-4059
Author(s):  
Lan-Zhe Guo ◽  
Feng Kuang ◽  
Zhang-Xun Liu ◽  
Yu-Feng Li ◽  
Nan Ma ◽  
...  
Keyword(s):  
Supervised Learning ◽  
User Experience ◽  
Large Scale ◽  
Gradient Methods ◽  
Sampling Bias ◽  
Weakly Supervised Learning ◽  
Label Noise ◽  
Weak Supervision ◽  
Ride Sharing ◽  
Weakly Supervised

Weakly supervised learning aims at coping with scarce labeled data. Previous weakly supervised studies typically assume that there is only one kind of weak supervision in data. In many applications, however, raw data usually contains more than one kind of weak supervision at the same time. For example, in user experience enhancement from Didi, one of the largest online ride-sharing platforms, the ride comment data contains severe label noise (due to the subjective factors of passengers) and severe label distribution bias (due to the sampling bias). We call such a problem as ‘compound weakly supervised learning’. In this paper, we propose the CWSL method to address this problem based on Didi ride-sharing comment data. Specifically, an instance reweighting strategy is employed to cope with severe label noise in comment data, where the weights for harmful noisy instances are small. Robust criteria like AUC rather than accuracy and the validation performance are optimized for the correction of biased data label. Alternating optimization and stochastic gradient methods accelerate the optimization on large-scale data. Experiments on Didi ride-sharing comment data clearly validate the effectiveness. We hope this work may shed some light on applying weakly supervised learning to complex real situations.

scholarly journals Weakly Supervised Learning Creates a Fusion of Modeling Cultures

2021 ◽  
Vol 7 (1) ◽  
pp. 203-211
Author(s):  
Chengliang Tang ◽  
Gan Yuan ◽  
Tian Zheng
Keyword(s):  
Supervised Learning ◽  
Weakly Supervised Learning ◽  
Weakly Supervised

Weakly Supervised Learning through Rank-based Contextual Measures

2021 ◽  
Author(s):  
Joao Gabriel Camacho Presotto ◽  
Lucas Pascotti Valem ◽  
Nikolas Gomes de Sa ◽  
Daniel Carlos Guimaraes Pedronette ◽  
Joao Paulo Papa
Keyword(s):  
Supervised Learning ◽  
Weakly Supervised Learning ◽  
Weakly Supervised
Sign in / Sign up

Export Citation Format

Share Document