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Deep Residual Learning for Image Recognition
Subject area: Science,Engineering and Technology · Area of research: Image Recognition
Abstract
Deep neural networks have potentially demonstrated dominant success in the field of image recognition and relevant tasks. However, with the significant increase of the network depth mostly leads to a specific set of optimization-based challenges, specifically in terms of vanishing as well as exploding the gradients.
How to cite this paper
Sai Dhiresh Kilari, Dr. Peter Wu "Deep Residual Learning for Image Recognition" Iconic Research And Engineering Journals Volume 6 Issue 1 2022 Page 780-783
Sai Dhiresh Kilari, Dr. Peter Wu "Deep Residual Learning for Image Recognition" Iconic Research And Engineering Journals, vol. 6, no. 1, Jul. 2022
Sai Dhiresh Kilari, Dr. Peter Wu (2022). Deep Residual Learning for Image Recognition. Iconic Research And Engineering Journals, 6(1).
Sai Dhiresh Kilari, Dr. Peter Wu "Deep Residual Learning for Image Recognition" Iconic Research And Engineering Journals, vol. 6, no. 1, Jul. 2022.
@article{1703688,
author = {Sai Dhiresh Kilari, Dr. Peter Wu},
title = {Deep Residual Learning for Image Recognition},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {6},
number = {1},
pages = {780-783},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1703688.pdf},
abstract = {Deep neural networks have potentially demonstrated dominant success in the field of image recognition and relevant tasks. However, with the significant increase of the network depth mostly leads to a specific set of optimization-based challenges, specifically in terms of vanishing as well as exploding the gradients.},
month = {July},
}