Home / Current Issue / Paper 1708530
Convolutional Neural Networks (CNN) for Enhancing Data Through Augmentation
Subject area: Science,Engineering and Technology · Area of research: Convolutional Neural Networks
Abstract
In deep learning, the success of Convolutional Neural Networks (CNNs) heavily relies on access to large, diverse datasets. However, acquiring extensive labeled data is often expensive, labor-intensive, or impractical in many real-world scenarios. Data augmentation has emerged as a crucial strategy to expand training datasets by generating new samples through various transformations. While traditional techniques like rotation, flipping, scaling, and cropping help, they often fall short in creating sufficiently diverse or semantically rich data. To address this limitation, our study investigates the use of CNNs as advanced tools for data augmentation. The primary aim of this research is to assess CNN-based augmentation methods that leverage learned representations to generate synthetic yet realistic images. Our proposed framework utilizes CNNs for feature-based augmentation, integrating approaches such as deep generative models, transfer learning, and transformations in feature space?moving beyond conventional pixel-level augmentations. We conducted experiments on standard image datasets including MNIST and CIFAR-10, comparing the performance of models trained with traditional augmentation against those using CNN-enhanced data. The results demonstrated a significant boost in classification accuracy and robustness when CNN-driven augmentation was applied. Importantly, the augmented datasets contributed more diverse and informative samples, leading to reduced overfitting and better generalization. These findings highlight CNNs? potential to revolutionize data augmentation by automating the generation of meaningful training data. This approach not only enhances model performance but also minimizes the dependency on manual data labeling?particularly impactful in fields with limited labeled data, such as medical imaging and autonomous vehicles. Future work will explore combining CNN-based augmentation with adversarial training and semi-supervised learning to further strengthen model resilience and efficiency in data-scarce environments.
Keywords
Convolutional Neural Networks, Data Augmentation, Deep Learning, Image Generation, Synthetic Data, Generalization
References
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How to cite this paper
@article{1708530,
author = {Rakesh Jindal, Amisha Naik, K L Ganatre, Rajat Gupta},
title = {Convolutional Neural Networks (CNN) for Enhancing Data Through Augmentation},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {6},
number = {6},
pages = {408-414},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708530.pdf},
abstract = {In deep learning, the success of Convolutional Neural Networks (CNNs) heavily relies on access to large, diverse datasets. However, acquiring extensive labeled data is often expensive, labor-intensive, or impractical in many real-world scenarios. Data augmentation has emerged as a crucial strategy to expand training datasets by generating new samples through various transformations. While traditional techniques like rotation, flipping, scaling, and cropping help, they often fall short in creating sufficiently diverse or semantically rich data. To address this limitation, our study investigates the use of CNNs as advanced tools for data augmentation. The primary aim of this research is to assess CNN-based augmentation methods that leverage learned representations to generate synthetic yet realistic images. Our proposed framework utilizes CNNs for feature-based augmentation, integrating approaches such as deep generative models, transfer learning, and transformations in feature space?moving beyond conventional pixel-level augmentations. We conducted experiments on standard image datasets including MNIST and CIFAR-10, comparing the performance of models trained with traditional augmentation against those using CNN-enhanced data. The results demonstrated a significant boost in classification accuracy and robustness when CNN-driven augmentation was applied. Importantly, the augmented datasets contributed more diverse and informative samples, leading to reduced overfitting and better generalization. These findings highlight CNNs? potential to revolutionize data augmentation by automating the generation of meaningful training data. This approach not only enhances model performance but also minimizes the dependency on manual data labeling?particularly impactful in fields with limited labeled data, such as medical imaging and autonomous vehicles. Future work will explore combining CNN-based augmentation with adversarial training and semi-supervised learning to further strengthen model resilience and efficiency in data-scarce environments.},
keywords = {Convolutional Neural Networks, Data Augmentation, Deep Learning, Image Generation, Synthetic Data, Generalization},
month = {December},
}