• Skip to main content
  • Skip to secondary menu
  • Skip to primary sidebar
  • Skip to footer
  • Home
  • Crypto Currency
  • Technology
  • Contact
NEO Share

NEO Share

Sharing The Latest Tech News

  • Home
  • Artificial Intelligence
  • Machine Learning
  • Computers
  • Mobile
  • Crypto Currency

Review — CB Loss: Class-Balanced Loss Based on Effective Number of Samples (Image Classification)

March 7, 2021 by systems

Using the Effective Number of Samples for Each Class to Re-Balance the Loss, Outperforms Focal Loss in RetinaNet

Sik-Ho Tsang

In this paper, Class-Balanced Loss Based on Effective Number of Samples, (CB Loss), by Cornell University, Cornell Tech, Google Brain, and Alphabet Inc., is reviewed. In this paper:

  • A re-weighting scheme is designed that uses the effective number of samples for each class to re-balance the loss, called class-balanced loss.

This is a paper in 2019 CVPR over 200 citations. (Sik-Ho Tsang @ Medium)

Filed Under: Artificial Intelligence

Primary Sidebar

Stay Ahead: The Latest Tech News and Innovations

Cryptocurrency Market Updates: What’s Happening Now

Emerging Trends in Artificial Intelligence: What to Watch For

Top Cloud Computing Services to Secure Your Data

The Future of Mobile Technology: Recent Advancements and Predictions

Footer

  • Privacy Policy
  • Terms and Conditions

Copyright © 2025 NEO Share

Terms and Conditions - Privacy Policy