A Comparison of CNN and Transformer in Continual Learning

Sammanfattning: Within the realm of computer vision tasks, Convolutional Neural Networks (CNN) and Transformers represent two predominant methodologies, often subject to extensive comparative analyses elucidating their respective merits and demerits. This thesis embarks on an exploration of these two models within the framework of continual learning, with a specific focus on their propensities for resisting catastrophic forgetting. We hypothesize that Transformer models exhibit a higher resilience to catastrophic forgetting in comparison to their CNN counterparts. To substantiate this hypothesis, a meticulously crafted experimental design was implemented, involving the selection of diverse models and continual learning approaches, and careful tuning of the networks to ensure an equitable comparison. In the majority of conducted experiments, encompassing both the contexts of class incremental learning settings and task incremental learning settings, our results substantiate the aforementioned hypothesis. Nevertheless, the insights garnered also underscore the necessity for more exhaustive and encompassing experimental evaluations to fully validate the asserted hypothesis.