Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise

Dan Hendrycks, Mantas Mazeika, Duncan Wilson, Kevin Gimpel

Nov-20-2025, 23:38:23 GMT–Neural Information Processing Systems

The growing importance of massive datasets used for deep learning makes robustness to label noise a critical property for classifiers to have.

classifier, machine learning, natural language, (20 more...)

Neural Information Processing Systems

Nov-20-2025, 23:38:23 GMT

Conferences PDF

Country:
- North America
  - United States
    - Nevada (0.04)
    - Illinois > Cook County
      - Chicago (0.04)
    - California > Alameda County
      - Berkeley (0.04)
  - Canada > Quebec
    - Montreal (0.04)
- Asia > Afghanistan
  - Parwan Province > Charikar (0.04)

Genre:
- Overview (0.34)

Technology:
- Information Technology
  - Communications (0.93)
  - Artificial Intelligence
    - Natural Language (1.00)
    - Machine Learning
      - Inductive Learning (0.69)
      - Neural Networks > Deep Learning (0.49)

Duplicate Docs Excel Report

Title
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise

Similar Docs Excel Report more

Title	Similarity	Source
None found