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APA 7th:

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. CVPR 2016.

MLA 9th:

He, et al. "Deep Residual Learning for Image Recognition." CVPR 2016, 2016.

BibTeX:

@misc{key2016, title={Deep Residual Learning for Image Recognition}, author={He, K., Zhang, X., Ren, S., & Sun, J.}, year={2016}}

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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/nmacgklsm

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.

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