Performance Analysis of Incremental Learning Strategy in Image Classification

Deep learning-based image classification model learns from the fixed and specific training dataset. For the generalization and adaptation of human learning behaviour, some of these models adapted incremental learning to enhance the learning and knowledge from updated and incremented dataset. An incremented dataset can be in form of increment of examples or new class dataset images. This incremented dataset is learned by deep learning models by two incremental learning strategies i.e. sample-wise and class-wise. This paper proposes a performance analysis methodoloiky and experimentally analyze the performance of these incremental learning strategies in CNN based image classification model on prepared incremented dataset. The evaluation of performance of these deep image classification model on classification performance metrics such as accuracy, precision, recall and Fl score shows that these model's learning and classification capabilities are increased with incremental learning on the incremented dataset. Two different incremental learning shows the relation between performances of model to the increment of dataset.