Analysis of Four Generator Architectures of C-Gan, Loss Function, and Annotation Method for Epiphyte Identification

Abstract. The deep learning (DL) models require timely updates to continue their reliability and robustness in prediction, classification, and segmentation tasks. When the deep learning models are tested with a limited test set, the model will not reveal the drawbacks. Every deep learning baseline model needs timely updates by incorporating more data, change in architecture, and hyper parameter tuning. This work focuses on updating the Conditional Generative Adversarial Network (C-GAN) based epiphyte identification deep learning model by incorporating 4 different generator architectures of GAN and two different loss functions. The four generator architectures used in this task are Resnet-6. Resnet-9, Resnet-50 and Resnet-101. A new annotation method called background removed annotation was tested to analyse the improvement in the epiphyte identification protocol. All the results obtained from the model by changing the above parameters are reported using two common evaluation metrics. Based on the parameter tuning experiment, Resnet-6, and Resnet- 9, with binary cross-entropy (BCE) as the loss function, attained higher scores also Resnet-6 with MSE as loss function performed well. The new annotation by removing the background had minimal effect on identifying the epiphytes.