A Domain Decomposition-Based CNN-DNN Architecture for Model Parallel Training Applied to Image Recognition Problems

• URL: http://arxiv.org/abs/2302.06564v2
• Date: Mon, 1 Jul 2024 19:12:49 GMT
• Title: A Domain Decomposition-Based CNN-DNN Architecture for Model Parallel Training Applied to Image Recognition Problems
• Authors: Axel Klawonn, Martin Lanser, Janine Weber,
• Abstract summary: A novel CNN-DNN architecture is proposed that naturally supports a model parallel training strategy. The proposed approach can significantly accelerate the required training time compared to the global model. Results show that the proposed approach can also help to improve the accuracy of the underlying classification problem.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks (DNNs) and, in particular, convolutional neural networks (CNNs) have brought significant advances in a wide range of modern computer application problems. However, the increasing availability of large amounts of datasets as well as the increasing available computational power of modern computers lead to a steady growth in the complexity and size of DNN and CNN models, respectively, and thus, to longer training times. Hence, various methods and attempts have been developed to accelerate and parallelize the training of complex network architectures. In this work, a novel CNN-DNN architecture is proposed that naturally supports a model parallel training strategy and that is loosely inspired by two-level domain decomposition methods (DDM). First, local CNN models, that is, subnetworks, are defined that operate on overlapping or nonoverlapping parts of the input data, for example, sub-images. The subnetworks can be trained completely in parallel and independently of each other. Each subnetwork then outputs a local decision for the given machine learning problem which is exclusively based on the respective local input data. Subsequently, in a second step, an additional DNN model is trained which evaluates the local decisions of the local subnetworks and generates a final, global decision. In this paper, we apply the proposed architecture to image classification problems using CNNs. Experimental results for different 2D image classification problems are provided as well as a face recognition problem, and a classification problem for 3D computer tomography (CT) scans. Therefore, classical ResNet and VGG architectures are considered. The results show that the proposed approach can significantly accelerate the required training time compared to the global model and, additionally, can also help to improve the accuracy of the underlying classification problem.

Related papers

• Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks [0.0]
  Two different domain decomposed CNN models are experimentally compared for different image classification problems. The resulting models show improved classification accuracies compared to the corresponding, composed global CNN model. A novel decomposed LDA strategy is proposed which also relies on a localization approach and which is combined with a small neural network model.
  arXiv  Detail & Related papers  (2024-10-30T18:07:12Z)
• Model Parallel Training and Transfer Learning for Convolutional Neural Networks by Domain Decomposition [0.0]
  Deep convolutional neural networks (CNNs) have been shown to be very successful in a wide range of image processing applications. Due to their increasing number of model parameters and an increasing availability of large amounts of training data, parallelization strategies to efficiently train complex CNNs are necessary.
  arXiv  Detail & Related papers  (2024-08-26T17:35:01Z)
• CNN2GNN: How to Bridge CNN with GNN [59.42117676779735]
  We propose a novel CNN2GNN framework to unify CNN and GNN together via distillation. The performance of distilled boosted'' two-layer GNN on Mini-ImageNet is much higher than CNN containing dozens of layers such as ResNet152.
  arXiv  Detail & Related papers  (2024-04-23T08:19:08Z)
• Neural Attentive Circuits [93.95502541529115]
  We introduce a general purpose, yet modular neural architecture called Neural Attentive Circuits (NACs) NACs learn the parameterization and a sparse connectivity of neural modules without using domain knowledge. NACs achieve an 8x speedup at inference time while losing less than 3% performance.
  arXiv  Detail & Related papers  (2022-10-14T18:00:07Z)
• Towards a General Purpose CNN for Long Range Dependencies in $\mathrm{N}$D [49.57261544331683]
  We propose a single CNN architecture equipped with continuous convolutional kernels for tasks on arbitrary resolution, dimensionality and length without structural changes. We show the generality of our approach by applying the same CCNN to a wide set of tasks on sequential (1$mathrmD$) and visual data (2$mathrmD$) Our CCNN performs competitively and often outperforms the current state-of-the-art across all tasks considered.
  arXiv  Detail & Related papers  (2022-06-07T15:48:02Z)
• A Novel Hand Gesture Detection and Recognition system based on ensemble-based Convolutional Neural Network [3.5665681694253903]
  Detection of hand portion has become a challenging task in computer vision and pattern recognition communities. Deep learning algorithm like convolutional neural network (CNN) architecture has become a very popular choice for classification tasks. In this paper, an ensemble of CNN-based approaches is presented to overcome some problems like high variance during prediction, overfitting problem and also prediction errors.
  arXiv  Detail & Related papers  (2022-02-25T06:46:58Z)
• Wide and Deep Graph Neural Networks with Distributed Online Learning [175.96910854433574]
  Graph neural networks (GNNs) learn representations from network data with naturally distributed architectures. Online learning can be used to retrain GNNs at testing time, overcoming this issue. This paper proposes the Wide and Deep GNN (WD-GNN), a novel architecture that can be easily updated with distributed online learning mechanisms.
  arXiv  Detail & Related papers  (2020-06-11T12:48:03Z)
• Computational optimization of convolutional neural networks using separated filters architecture [69.73393478582027]
  We consider a convolutional neural network transformation that reduces computation complexity and thus speedups neural network processing. Use of convolutional neural networks (CNN) is the standard approach to image recognition despite the fact they can be too computationally demanding.
  arXiv  Detail & Related papers  (2020-02-18T17:42:13Z)
• Inferring Convolutional Neural Networks' accuracies from their architectural characterizations [0.0]
  We study the relationships between a CNN's architecture and its performance. We show that the attributes can be predictive of the networks' performance in two specific computer vision-based physics problems. We use machine learning models to predict whether a network can perform better than a certain threshold accuracy before training.
  arXiv  Detail & Related papers  (2020-01-07T16:41:58Z)
• Approximation and Non-parametric Estimation of ResNet-type Convolutional Neural Networks [52.972605601174955]
  We show a ResNet-type CNN can attain the minimax optimal error rates in important function classes. We derive approximation and estimation error rates of the aformentioned type of CNNs for the Barron and H"older classes.
  arXiv  Detail & Related papers  (2019-03-24T19:42:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.