Model Parallel Training and Transfer Learning for Convolutional Neural Networks by Domain Decomposition

• URL: http://arxiv.org/abs/2408.14442v1
• Date: Mon, 26 Aug 2024 17:35:01 GMT
• Title: Model Parallel Training and Transfer Learning for Convolutional Neural Networks by Domain Decomposition
• Authors: Axel Klawonn, Martin Lanser, Janine Weber,
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep convolutional neural networks (CNNs) have been shown to be very successful in a wide range of image processing applications. However, 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. In previous work by the authors, a novel model parallel CNN architecture was proposed which is loosely inspired by domain decomposition. In particular, the novel network architecture is based on a decomposition of the input data into smaller subimages. For each of these subimages, local CNNs with a proportionally smaller number of parameters are trained in parallel and the resulting local classifications are then aggregated in a second step by a dense feedforward neural network (DNN). In the present work, we compare the resulting CNN-DNN architecture to less costly alternatives to combine the local classifications into a final, global decision. Additionally, we investigate the performance of the CNN-DNN trained as one coherent model as well as using a transfer learning strategy, where the parameters of the pre-trained local CNNs are used as initial values for a subsequently trained global coherent CNN-DNN model.

Related papers

• 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)
• A Gradient Boosting Approach for Training Convolutional and Deep Neural Networks [0.0]
  We introduce two procedures for training Convolutional Neural Networks (CNNs) and Deep Neural Network based on Gradient Boosting (GB) The presented models show superior performance in terms of classification accuracy with respect to standard CNN and Deep-NN with the same architectures.
  arXiv  Detail & Related papers  (2023-02-22T12:17:32Z)
• A Domain Decomposition-Based CNN-DNN Architecture for Model Parallel Training Applied to Image Recognition Problems [0.0]
  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.
  arXiv  Detail & Related papers  (2023-02-13T18:06:59Z)
• 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)
• Exploiting Hybrid Models of Tensor-Train Networks for Spoken Command Recognition [9.262289183808035]
  This work aims to design a low complexity spoken command recognition (SCR) system. We exploit a deep hybrid architecture of a tensor-train (TT) network to build an end-to-end SRC pipeline. Our proposed CNN+(TT-DNN) model attains a competitive accuracy of 96.31% with 4 times fewer model parameters than the CNN model.
  arXiv  Detail & Related papers  (2022-01-11T05:57:38Z)
• An Alternative Practice of Tropical Convolution to Traditional Convolutional Neural Networks [0.5837881923712392]
  We propose a new type of CNNs called Tropical Convolutional Neural Networks (TCNNs) TCNNs are built on tropical convolutions in which the multiplications and additions in conventional convolutional layers are replaced by additions and min/max operations respectively. We show that TCNN can achieve higher expressive power than ordinary convolutional layers on the MNIST and CIFAR10 image data set.
  arXiv  Detail & Related papers  (2021-03-03T00:13:30Z)
• Local Critic Training for Model-Parallel Learning of Deep Neural Networks [94.69202357137452]
  We propose a novel model-parallel learning method, called local critic training. We show that the proposed approach successfully decouples the update process of the layer groups for both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) We also show that trained networks by the proposed method can be used for structural optimization.
  arXiv  Detail & Related papers  (2021-02-03T09:30:45Z)
• ACDC: Weight Sharing in Atom-Coefficient Decomposed Convolution [57.635467829558664]
  We introduce a structural regularization across convolutional kernels in a CNN. We show that CNNs now maintain performance with dramatic reduction in parameters and computations.
  arXiv  Detail & Related papers  (2020-09-04T20:41:47Z)
• Exploring Deep Hybrid Tensor-to-Vector Network Architectures for Regression Based Speech Enhancement [53.47564132861866]
  We find that a hybrid architecture, namely CNN-TT, is capable of maintaining a good quality performance with a reduced model parameter size. CNN-TT is composed of several convolutional layers at the bottom for feature extraction to improve speech quality.
  arXiv  Detail & Related papers  (2020-07-25T22:21:05Z)
• 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.