The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Improved Adam Optimizer for Deep Neural Networks
Abstract
Adaptive optimization algorithms, such as Adam and RMSprop, have witnessed better optimization performance than stochastic gradient descent (SGD) in some scenarios. However, recent studies show that they often lead to worse generalization performance than SGD, especially for training deep neural networks (DNNs). In this work, we identify the reasons that Adam generalizes worse than SGD, and develop a variant of Adam to eliminate the generalization gap. The proposed method, normalized direction-preserving Adam (ND-Adam), enables more precise control of the direction and step size for updating weight vectors, leading to significantly improved generalization performance. Following a similar rationale, we further improve the generalization performance in classification tasks by regularizing the softmax logits. By bridging the gap between SGD and Adam, we also hope to shed light on why certain optimization algorithms generalize better than others.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
Squeeze-and-Excitation Networks
Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2913372 · Cited by 12,157
Zagoruyko, S., & Komodakis, N. (2016) · Procedings of the British Machine Vision Conference 2016 · doi:10.5244/c.30.87 · Cited by 7,244
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322
Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., & Vinyals, O. (2016) · arXiv · doi:10.48550/arxiv.1611.03530 · Cited by 2,838 · cited 1×
“Furthermore, recent work has shown that DNNs are capable of fitting noise data [9], suggesting that their generalization”
Background
Research works printed but not linked: 6
- duchi, 2011, Adaptive subgradient methods for online learning and stochastic optimization, Journal of Machine Learning Research, 12, 2121
- arpit, 2017, A closer look at memorization in deep networks
- tieleman, 2012, Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude, COURSERA Neural Networks for Machine Learning
- 10.1162/neco.1997.9.1.1 · DOI · Paper page
- wilson, 2017, The marginal value of adaptive gradient methods in machine learning, Advances in neural information processing systems
- 10.1109/CVPR.2015.7298594 · DOI · Paper page
Cited by
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906
Recurrent Neural Networks: A Comprehensive Review of Architectures, Variants, and Applications
Mienye, I. D., Swart, T. G., & Obaido, G. (2024) · Information · doi:10.3390/info15090517 · Cited by 732
Taye, M. M. (2023) · Computation · doi:10.3390/computation11030052 · Cited by 629
Cyber Security Threats Detection in Internet of Things Using Deep Learning Approach
Ullah, F., Naeem, H., Jabbar, S., Khalid, S., Latif, M. A., Al-turjman, F., & Mostarda, L. (2019) · IEEE Access · doi:10.1109/access.2019.2937347 · Cited by 266
A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data
Leevy, J. L., & Khoshgoftaar, T. M. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00382-x · Cited by 264
Metadata
- Authors
- Zhang, Z.
- Venue
- 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 1-2
- OpenAlex
- W2912290085
- Cited by
- 1,400
- References
- 7
- FWCI
- 22.05
- Type
- conference paper
- Language
- en
- Open access
- closed
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