Data Augmentation and Convolutional Network Architecture Influence on Distributed Learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914386435637248 |
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| author | Jansen, Victor Forattini Martins, Emanuel Teixeira Lima, Yasmin Souza Silva, Flavio de Oliveira Moreira, Rodrigo Moreira, Larissa Ferreira Rodrigues |
| author_facet | Jansen, Victor Forattini Martins, Emanuel Teixeira Lima, Yasmin Souza Silva, Flavio de Oliveira Moreira, Rodrigo Moreira, Larissa Ferreira Rodrigues |
| contents | Convolutional Neural Networks (CNNs) have proven to be highly effective in solving a broad spectrum of computer vision tasks, such as classification, identification, and segmentation. These methods can be deployed in both centralized and distributed environments, depending on the computational demands of the task. While much of the literature has focused on the explainability of CNNs, which is essential for building trust and confidence in their predictions, there remains a gap in understanding their impact on computational resources, particularly in distributed training contexts. In this study, we analyze how CNN architectures primarily influence model accuracy and investigate additional factors that affect computational efficiency in distributed systems. Our findings contribute valuable insights for optimizing the deployment of CNNs in resource-intensive scenarios, paving the way for further exploration of variables critical to distributed learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_10902 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Data Augmentation and Convolutional Network Architecture Influence on Distributed Learning Jansen, Victor Forattini Martins, Emanuel Teixeira Lima, Yasmin Souza Silva, Flavio de Oliveira Moreira, Rodrigo Moreira, Larissa Ferreira Rodrigues Distributed, Parallel, and Cluster Computing Convolutional Neural Networks (CNNs) have proven to be highly effective in solving a broad spectrum of computer vision tasks, such as classification, identification, and segmentation. These methods can be deployed in both centralized and distributed environments, depending on the computational demands of the task. While much of the literature has focused on the explainability of CNNs, which is essential for building trust and confidence in their predictions, there remains a gap in understanding their impact on computational resources, particularly in distributed training contexts. In this study, we analyze how CNN architectures primarily influence model accuracy and investigate additional factors that affect computational efficiency in distributed systems. Our findings contribute valuable insights for optimizing the deployment of CNNs in resource-intensive scenarios, paving the way for further exploration of variables critical to distributed learning. |
| title | Data Augmentation and Convolutional Network Architecture Influence on Distributed Learning |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2603.10902 |