Multiscale Stochastic Gradient Descent: Efficiently Training Convolutional Neural Networks

• URL: http://arxiv.org/abs/2501.12739v2
• Date: Wed, 12 Mar 2025 16:05:08 GMT
• Title: Multiscale Stochastic Gradient Descent: Efficiently Training Convolutional Neural Networks
• Authors: Niloufar Zakariaei, Shadab Ahamed, Eldad Haber, Moshe Eliasof,
• Abstract summary: Multiscale Gradient Descent (Multiscale-SGD) is a novel optimization approach that exploits coarse-to-fine training strategies to estimate the gradient at a fraction of the cost.<n>We introduce a new class of learnable, scale-independent Mesh-Free Convolutions (MFCs) that ensure consistent gradient behavior across resolutions.<n>Our results establish a new paradigm for the efficient training of deep networks, enabling practical scalability in high-resolution and multiscale learning tasks.
• Score: 6.805997961535213
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Stochastic Gradient Descent (SGD) is the foundation of modern deep learning optimization but becomes increasingly inefficient when training convolutional neural networks (CNNs) on high-resolution data. This paper introduces Multiscale Stochastic Gradient Descent (Multiscale-SGD), a novel optimization approach that exploits coarse-to-fine training strategies to estimate the gradient at a fraction of the cost, improving the computational efficiency of SGD type methods while preserving model accuracy. We derive theoretical criteria for Multiscale-SGD to be effective, and show that while standard convolutions can be used, they can be suboptimal for noisy data. This leads us to introduce a new class of learnable, scale-independent Mesh-Free Convolutions (MFCs) that ensure consistent gradient behavior across resolutions, making them well-suited for multiscale training. Through extensive empirical validation, we demonstrate that in practice, (i) our Multiscale-SGD approach can be used to train various architectures for a variety of tasks, and (ii) when the noise is not significant, standard convolutions benefit from our multiscale training framework. Our results establish a new paradigm for the efficient training of deep networks, enabling practical scalability in high-resolution and multiscale learning tasks.

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