Quality Loss Minimization
Quality Loss Minimization is a methodological framework within loss reduction that specifically aims to reduce performance degradation in machine learning models by optimizing the discrepancy between predicted and true quality levels during training.
228
Publications
9.9K
Citations
639
Authors
270
Institutions
Publications per year
2017–2026
75
Parents
228
GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving
Buyu Li, Wanli Ouyang, Lu Sheng et al. · 2019 · 383 citations
Rosa M. Raybaudi‐Massilia, Jonathan Mosqueda‐Melgar, Robert Soliva‐Fortuny et al. · Comprehensive Reviews in Food Science and Food Safety · 2009 · 347 citations
1–5 of 228
639
9
h-index
Southern Taiwan University of Science and Technology, Taiwan
5 publications · 80 citations
5
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4
h-index
4
h-index
4
h-index
1–5 of 639
270
Feng Chia University
Taichung, Taiwan
10 h-index
12 publications
263 citations
Clemson University
Clemson, United States
8 h-index
14 publications
338 citations

Southern Taiwan University of Science and Technology
Tainan City, Taiwan
7 h-index
11 publications
159 citations

Peking University
Beijing, China
6 h-index
12 publications
760 citations
Tsinghua University
Beijing, China
5 h-index
8 publications
253 citations
National University of Singapore
Singapore, Singapore
5 h-index
11 publications
496 citations

Shanghai Jiao Tong University
Shanghai, China
5 h-index
15 publications
1.3K citations

Beihang University
Beijing, China
5 h-index
19 publications
1.8K citations
1–8 of 270
17 publications · 531 citations
16
h-index
10 publications · 363 citations
10
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12 publications · 274 citations
10
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5 publications · 428 citations
5
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5 publications · 95 citations
5
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