Leveraging Guided Backpropagation to Select Convolutional Neural Networks for Plant Classification.

ABSTRACT: The development of state-of-the-art convolutional neural networks (CNN) has allowed researchers to perform plant classification tasks previously thought impossible and rely on human judgment. Researchers often develop complex CNN models to achieve better performances, introducing over-parameterization and forcing the model to overfit on a training dataset. The most popular process for evaluating overfitting in a deep learning model is using accuracy and loss curves. Train and loss curves may help understand the performance of a model but do not provide guidance on how the model could be modified to attain better performance. In this article, we analyzed the relation between the features learned by a model and its capacity and showed that a model with higher representational capacity might

SUBMITTER: Mostafa S 

PROVIDER: S-EPMC9132261 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature