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Learning Concept Embeddings with Combined Human-Machine Expertise
v1v2 (latest)

Learning Concept Embeddings with Combined Human-Machine Expertise

24 September 2015
Michael J. Wilber
Iljung S. Kwak
D. Kriegman
Serge J. Belongie
ArXiv (abs)PDFHTML

Papers citing "Learning Concept Embeddings with Combined Human-Machine Expertise"

15 / 15 papers shown
Title
Bayesian Metric Learning for Uncertainty Quantification in Image
  Retrieval
Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval
Frederik Warburg
M. Miani
Silas Brack
Søren Hauberg
UQCVBDL
54
7
0
02 Feb 2023
PENCIL: Deep Learning with Noisy Labels
PENCIL: Deep Learning with Noisy Labels
Kun Yi
G. Wang
Jianxin Wu
NoLa
56
2
0
17 Feb 2022
Understanding How Dimension Reduction Tools Work: An Empirical Approach
  to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization
Yingfan Wang
Haiyang Huang
Cynthia Rudin
Yaron Shaposhnik
290
319
0
08 Dec 2020
Theme-Matters: Fashion Compatibility Learning via Theme Attention
Theme-Matters: Fashion Compatibility Learning via Theme Attention
Jui-Hsin Lai
Bo Wu
Xin Wang
Dan Zeng
Tao Mei
Jingen Liu
71
9
0
12 Dec 2019
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive
  Step Size
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size
Ke Ma
Jinshan Zeng
Qianqian Xu
Xiaochun Cao
Wei Liu
Yuan Yao
67
3
0
01 Dec 2019
Active Ordinal Querying for Tuplewise Similarity Learning
Active Ordinal Querying for Tuplewise Similarity Learning
Gregory H. Canal
Stefano Fenu
Christopher Rozell
DML
70
8
0
09 Oct 2019
Active embedding search via noisy paired comparisons
Active embedding search via noisy paired comparisons
Gregory H. Canal
A. Massimino
Mark A. Davenport
Christopher Rozell
87
24
0
10 May 2019
Probabilistic End-to-end Noise Correction for Learning with Noisy Labels
Probabilistic End-to-end Noise Correction for Learning with Noisy Labels
Kun Yi
Jianxin Wu
NoLa
72
418
0
19 Mar 2019
Less but Better: Generalization Enhancement of Ordinal Embedding via
  Distributional Margin
Less but Better: Generalization Enhancement of Ordinal Embedding via Distributional Margin
Ke Ma
Qianqian Xu
Zhiyong Yang
Xiaochun Cao
37
1
0
05 Dec 2018
Comparison-Based Random Forests
Comparison-Based Random Forests
Siavash Haghiri
Damien Garreau
U. V. Luxburg
119
25
0
18 Jun 2018
Multi-Attention Multi-Class Constraint for Fine-grained Image
  Recognition
Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition
Ming Sun
Yuchen Yuan
Feng Zhou
Errui Ding
172
352
0
14 Jun 2018
Personalized Classifier for Food Image Recognition
Personalized Classifier for Food Image Recognition
Shota Horiguchi
Sosuke Amano
Makoto Ogawa
Kiyoharu Aizawa
60
71
0
08 Apr 2018
The State of the Art in Integrating Machine Learning into Visual
  Analytics
The State of the Art in Integrating Machine Learning into Visual Analytics
Alex Endert
W. Ribarsky
C. Turkay
B. Wong
I. Nabney
Ignacio Díaz Blanco
Fabrice Rossi
53
215
0
22 Feb 2018
Integrating Scene Text and Visual Appearance for Fine-Grained Image
  Classification
Integrating Scene Text and Visual Appearance for Fine-Grained Image Classification
X. Bai
Mingkun Yang
Pengyuan Lyu
Yongchao Xu
Jiebo Luo
101
75
0
15 Apr 2017
Fine-grained Image Classification by Exploring Bipartite-Graph Labels
Fine-grained Image Classification by Exploring Bipartite-Graph Labels
Feng Zhou
Yuanqing Lin
97
131
0
08 Dec 2015
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