ResLT: Residual Learning for Long-tailed Recognition

ResLT: Residual Learning for Long-tailed Recognition

Deep learning algorithms face great challenges with long-tailed data distribution which, however, is quite a common case in real-world scenarios. Previous methods tackle the problem from either the aspect of input space (re-sampling classes with different frequencies) or loss space (re-weighting cla...

Full description

Bibliographic Details
Main Authors: Cui, J. (Author), Jia, J. (Author), Liu, S. (Author), Tian, Z. (Author), Zhong, Z. (Author)
Format: Article
Language:English
Published: IEEE Computer Society 2022
Subjects:
Computer vision
Data distribution
Deep learning
Head
Image enhancement
Image recognition
Imbalanced Learning
Input space
Learning algorithms
Long-tailed recognition
Long-tailed Recognition
Magnetic heads
Real-world scenario
Representation learning
Residual learning
Residual Learning
Tail
Training
Transfer learning
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items