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Published in:
2023 | OriginalPaper | Chapter
Sensitivity Analysis of Regularization Techniques in Convolution Neural Networks with Tensorflow
Authors : Shivakumar Dalali, B. E. ManjunathSwamy, Giridhar Gowda, N. S. Girish Rao Salanke
Published in: ICDSMLA 2021
Publisher: Springer Nature Singapore
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Abstract
Achieving excellent performance in the fields of classification, detection, and recognition of objects in pictures and videos, Convolution Neural Networks (CNN) have been developed. Different Regularization Techniques have an impact on the performance of Convolution Neural Networks. By Applying Regularization Techniques such as Early halting, L2 Norm, Dropout, and data augmentation approach, we may improve the model performance significantly. The integration of all of these Regularization Techniques results in a drastic improvements in the performance of the neural network model. Model’s accuracy will improve up to 90%–95% and also reduces the over fitting problems, where results are predicting with respect to training model. This paper highlights the impact of various regularization techniques that boosts the learning convergence.