Deep Residual Learning for Image Recognition: A Survey
Guangzhou University · Harbin Institute of Technology · +1 more institution
Abstract
Deep Residual Networks have recently been shown to significantly improve the performance of neural networks trained on ImageNet, with results beating all previous methods on this dataset by large margins in the image classification task. However, the meaning of these impressive numbers and their implications for future research are not fully understood yet. In this survey, we will try to explain what Deep Residual Networks are, how they achieve their excellent results, and why their successful implementation in practice represents a significant advance over existing techniques. We also discuss some open questions related to residual learning as well as possible applications of Deep Residual Networks beyond…
Citation impact
- FWCI
- 81.39
- Percentile
- 100%
- References
- 91
Authors
2Topics & keywords
- Residual
- Deep learning
- Computer science
- Artificial intelligence
- Deep neural networks
- Meaning (existential)
- Task (project management)
- Machine learning