Deep Residual Learning for Image Recognition

View details
Paper Library
URL
Date
2016
Review Date
2026/03/09 02:41
Review Type
R
Key Findings
Proposed residual learning framework with skip connections to enable training of very deep networks (up to 152 layers). Won 1st place on ImageNet classification, detection, and localization tasks. Demonstrated that network depth is crucial for visual recognition tasks.
Venue
CVPR 2016
Review Status
Completed
Field
Computer Vision
Authors
He, Zhang, Ren, Sun
Rating
6 - Outstanding
Rating
6 - Outstanding
Authors
He, Zhang, Ren, Sun
Date
2016
Review Status
Completed