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Deep Residual Learning for Image Recognition
Type: Conference Paper
Authors: He, K., Zhang, X., Ren, S., & Sun, J.
Source: CVPR 2016
Year: 2016
Citations: 150000
DOI: 10.1145/lary5q4et
Language: English
Abstract
We present a residual learning framework to ease the training of networks that are substantially deeper.
(Simulated Full Text Content)
1. Introduction
The rapid development of Deep Residual Learning for Image Recognition has revolutionized the field. This paper explores the fundamental principles...
2. Methodology
We employed a dataset consisting of over 50,000 samples... The algorithm was optimized using stochastic gradient descent...
3. Results
Our experiments demonstrate a significant improvement over baseline models (see Table 1). The 2016 publication date marks a significant milestone in this domain.