Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/ILL

ILL

Reported on 23 benchmarks across 3 tasks · 1 paper · 9 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision8 results

Image Classificationonmini WebVision 1.0
Top 1 Accuracy· 2023-05-22
79.37
SOTA
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Image ClassificationonCIFAR-10N-Random2
Accuracy (mean)· 2023-05-22
95.04
best: 96.21 (PSSCL)
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Image ClassificationonCIFAR-10N-Random3
Accuracy (mean)· 2023-05-22
95.13
best: 96.49 (PSSCL)
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Image ClassificationonCIFAR-10N-Aggregate
Accuracy (mean)· 2023-05-22
95.47
best: 97.39 (ProMix)
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Image ClassificationonCIFAR-10N-Random1
Accuracy (mean)· 2023-05-22
94.85
best: 96.97 (ProMix)
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Image ClassificationonCIFAR-100N
Accuracy (mean)· 2023-05-22
65.84
best: 74.08 (PGDF)
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Image ClassificationonClothing1M
Test Accuracy· 2023-05-22
74.02
best: 75.2 (Knockoffs-SPR)
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Image ClassificationonCIFAR-10N-Worst
Accuracy (mean)· 2023-05-22
93.58
best: 96.16 (ProMix)
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715

Medical8 results

Document Text Classificationonmini WebVision 1.0
Top 1 Accuracy· 2023-05-22
79.37
SOTA
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Document Text ClassificationonCIFAR-10N-Random2
Accuracy (mean)· 2023-05-22
95.04
best: 96.21 (PSSCL)
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Document Text ClassificationonCIFAR-10N-Random3
Accuracy (mean)· 2023-05-22
95.13
best: 96.49 (PSSCL)
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Document Text ClassificationonCIFAR-10N-Aggregate
Accuracy (mean)· 2023-05-22
95.47
best: 97.39 (ProMix)
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Document Text ClassificationonCIFAR-10N-Random1
Accuracy (mean)· 2023-05-22
94.85
best: 96.97 (ProMix)
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Document Text ClassificationonCIFAR-100N
Accuracy (mean)· 2023-05-22
65.84
best: 74.08 (PGDF)
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Document Text ClassificationonClothing1M
Test Accuracy· 2023-05-22
74.02
best: 75.2 (Knockoffs-SPR)
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Document Text ClassificationonCIFAR-10N-Worst
Accuracy (mean)· 2023-05-22
93.58
best: 96.16 (ProMix)
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715

Methodology7 results

Partial Label LearningonCIFAR-10 (partial ratio 0.1)
Accuracy· 2023-05-22
96.37
SOTA
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Partial Label LearningonCIFAR-100 (partial ratio 0.05)
Accuracy· 2023-05-22
74.58
SOTA
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Partial Label LearningonCIFAR-100 (partial ratio 0.01)
Accuracy· 2023-05-22
75.31
SOTA
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Partial Label LearningonCaltech-UCSD Birds 200 (partial ratio 0.05)
Accuracy· 2023-05-22
70.77
SOTA
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Partial Label LearningonCIFAR-10 (partial ratio 0.5)
Accuracy· 2023-05-22
95.91
SOTA
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Partial Label LearningonCIFAR-100 (partial ratio 0.1)
Accuracy· 2023-05-22
74
SOTA
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715
Partial Label LearningonCIFAR-10 (partial ratio 0.3)
Accuracy· 2023-05-22
96.26
SOTA
Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations arXiv:2305.12715