Enhancing Continual Learning with IMEX-Reg: A Robust Approach to Mitigate Catastrophic Forgetting

The ability of systems to adapt over time without losing previous knowledge, known as continual learning (CL), poses a significant challenge. While adept at processing large amounts of data, neural networks often suffer from catastrophic forgetting, where acquiring new information can erase what was learned previously. This phenomenon is particularly problematic in environments with restricted […]

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