Continual Learning and Catastrophic Forgetting — part 5/5

Cleaned-text code points [61992, 63548). Bibliography omitted here; source.md includes it.

We have further reviewed six computational approaches for continual learning. Replay, parameter regularization and functional regularization operate by making changes to the loss function, while optimization-based approaches change the way in which a given loss function is optimized. Context-dependent processing distributes computations across the network based on contextual or task-specific information, and template-based classification enables distinguishing objects or categories that are not observed together without having to directly learn discriminative boundaries.

As deep learning navigates the intricate challenges posed by non-stationary environments and catastrophic forgetting, it becomes evident that continual learning is not just a technical necessity, but a philosophical shift in the approach to artificial intelligence. Approaching this from an interdisciplinary collaboration, drawing inspiration from neuroscience, cognitive science, and psychology to imbue continual learning into artificial systems, seems to hold promise. Similarly, but in the reverse direction, insights gained in the deep learning field while doing so can help to unravel the computational principles that underlie the cognitive skill of continual learning in the brain.

### Acknowledgements

This effort is partially supported by the NSF EFRI BRAID Award #2317706 and the NSF PARTNER AI Institute NAIAD Award #2332744, as well as by funding from the European Union under Horizon Europe (Marie Skłodowska-Curie fellowship, grant agreement No. 101067759).