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/NCR (ResNet-18)

NCR (ResNet-18)

Reported on 9 benchmarks across 2 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 Vision6 results

Image ClassificationonRed MiniImageNet 20% label noise
Accuracy· 2022-02-04
69
SOTA
Learning with Neighbor Consistency for Noisy Labels arXiv:2202.02200
Image ClassificationonRed MiniImageNet 40% label noise
Accuracy· 2022-02-04
64.6
SOTA
Learning with Neighbor Consistency for Noisy Labels arXiv:2202.02200
Image ClassificationonRed MiniImageNet 80% label noise
Accuracy· 2022-02-04
51.2
SOTA
Learning with Neighbor Consistency for Noisy Labels arXiv:2202.02200
Image ClassificationonRed MiniImageNet 80% label noise
Test Accuracy· 2022-02-04
51.2
SOTA
Learning with Neighbor Consistency for Noisy Labels arXiv:2202.02200
Image ClassificationonRed MiniImageNet 40% label noise
Test Accuracy· 2022-02-04
64.6
SOTA
Learning with Neighbor Consistency for Noisy Labels arXiv:2202.02200
Image ClassificationonRed MiniImageNet 20% label noise
Test Accuracy· 2022-02-04
69
SOTA
Learning with Neighbor Consistency for Noisy Labels arXiv:2202.02200

Medical3 results

Document Text ClassificationonRed MiniImageNet 80% label noise
Test Accuracy· 2022-02-04
51.2
SOTA
Learning with Neighbor Consistency for Noisy Labels arXiv:2202.02200
Document Text ClassificationonRed MiniImageNet 40% label noise
Test Accuracy· 2022-02-04
64.6
SOTA
Learning with Neighbor Consistency for Noisy Labels arXiv:2202.02200
Document Text ClassificationonRed MiniImageNet 20% label noise
Test Accuracy· 2022-02-04
69
SOTA
Learning with Neighbor Consistency for Noisy Labels arXiv:2202.02200