Synergistic insights: Exploring continuous learning and explainable AI in handwritten digit recognition

Abstract

Deep Neural Networks achieve outstanding results; however, their reliance on a static environment with fixed data poses challenges in dynamic scenarios where data continuously evolves. Being capable of learning, adapting, and generalizing continually in a scalable, successful, and efficient manner is crucial for the sustainable development of AI systems. The classical solution of retraining the model using both old and new data is time-consuming and expensive. Continual Learning tackles the problem of learning new data distributions without the need for retraining from scratch. Furthermore, the task of recognizing unlabeled images using previously acquired knowledge becomes challenging, particularly when the new data needs to be incrementally annotated without starting the training process from scratch.To gain a deeper understanding of how “Black Box” neural networks make decisions, it is important to visualize components inside the model that affect the error rate throughout the decision-making process. The Continual Self-Learning model on label-less historical digits yields increasingly perceptive interpretations. This paper aims to establish a literature review of the latest advances in continual learning for computer vision tasks, to articulate catastrophic forgetting using Explainable Artificial Intelligence on both split MNIST and the historical digit dataset DIDA, and to shed light on important but still understudied topics.