Novel continual learning techniques on noisy label datasets

Many Machine Learning and Deep Learning algorithms are widely used with remarkable success in scenarios whose benchmark datasets consist of reliable data. However, they often struggle to handle realistic scenarios, particularly those in the financial sector, where available data constantly vary, increase daily, and may contain noise. As a result, we present an overview of the ongoing research at the AImageLab research laboratory of the University of Modena and Reggio Emilia, in collaboration with AxyonAI, focused on exploring Continual Learning methods in the presence of noisy data, with a special focus on noisy labels. To the best of our knowledge, this is a problem that has received limited attention from the scientific community thus far.