What is the thesis about?
Artificial Intelligence (AI) is having an enormous impact across diverse application fields, from autonomous driving to drug discovery. This progress is predominantly driven by deep learning, a technology whose performance scales with data availability. As the demand for large datasets grows, researchers have developed transfer learning as an alternative strategy to leverage pre-trained models. However, transfer learning faces several challenges, including the need for extensive annotated data, difficulty in adapting to highly disjoint domains, and the lack of knowledge accumulation when adapting to new domains. Continual learning emerges as a promising approach to address these issues, enabling models to learn from shifting data distributions without forgetting previously acquired knowledge.
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