Optimizing Computational Efficiency and Model Robustness through Adaptive Deep Learning Pipelines with Layerwise Gradient Modulation
Keywords:
Deep Learning, Computational Efficiency, Robustness, Gradient Modulation, Layerwise Optimization, Adaptive Training, Neural NetworksAbstract
The exponential growth in model complexity has imposed a dual challenge of maintaining computational efficiency while ensuring robustness in deep learning systems. This paper presents an adaptive pipeline framework that integrates layerwise gradient modulation (LGM) to address these issues. By dynamically adjusting gradient scaling across layers based on performance feedback, we achieve notable improvements in convergence stability and resource utilization. Experimental evaluations across convolutional neural networks (CNNs) and transformer architectures demonstrate up to 23% faster convergence and a 15–21% improvement in robustness to adversarial perturbations. This work paves the way for more efficient and fault-tolerant deep learning systems.
References
Dean, Jeffrey, et al. "Large scale distributed deep networks." Advances in Neural Information Processing Systems, 2012, pp. 1223–1231.
Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy. "Explaining and harnessing adversarial examples." International Conference on Learning Representations (ICLR), 2015.
He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
Ioffe, Sergey, and Christian Szegedy. "Batch normalization: Accelerating deep network training by reducing internal covariate shift." International Conference on Machine Learning (ICML), 2015, pp. 448–456.
Pascanu, Razvan, Tomas Mikolov, and Yoshua Bengio. "On the difficulty of training recurrent neural networks." International Conference on Machine Learning (ICML), 2013, pp. 1310–1318.
Srivastava, Rupesh Kumar, Klaus Greff, and Jürgen Schmidhuber. "Highway networks." arXiv preprint arXiv:1505.00387, 2015.
Szegedy, Christian, et al. "Intriguing properties of neural networks." International Conference on Learning Representations (ICLR), 2014.
Zeiler, Matthew D., and Rob Fergus. "Visualizing and understanding convolutional networks." European Conference on Computer Vision (ECCV), 2014, pp. 818–833.
Tang, Siyuan, et al. "Layerwise Optimization by Gradient Decomposition for Continual Learning." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
Simon, Christian, et al. "On Modulating the Gradient for Meta-Learning." European Conference on Computer Vision (ECCV), 2020, pp. 548–565.
Shi, Shaohuai, et al. "Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees." arXiv preprint arXiv:1911.08727, 2019.
Wang, Yifan, and Liang Wang. "Enriching Variety of Layer-Wise Learning Information by Gradient Combination." IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2019.
Varma, Karthik, et al. "LEGATO: A Layerwise Gradient Aggregation Algorithm for Mitigating Byzantine Attacks in Federated Learning." arXiv preprint arXiv:2107.12490, 2021.
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Copyright (c) 2022 Robert M Adamson (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
