Incremental Learning in Biological and Machine Learning Systems

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General Incremental Learning with Domain-aware Categorical Representations (2022, background): “In order to achieve human-level intelligence, it is indispensable for a learning system to continuously accumulate knowledge over time in an ever-changing environment, known as continual or incremental learning [5].”
On Learning the Geodesic Path for Incremental Learning (2021, background): “In fact, human minds learn from one task to another task by gradual walk as described in [3].”
Incremental training of first order recurrent neural networks to predict a context-sensitive language (2003, background): “More generally, incremental learning with neural networks can be characterised as any learning scheme that changes, in a staged and directed way, either the structure of the neural network, the learning parameters, the complexity of the input data, or a combination of these (Chalup, 2002).”
Domain adaptation for object recognition: An unsupervised approach (2011, background): “Since humans adapt (better) between extreme domains if they 'gradually' walk through the path between the domains (e.g. [34] [12]), we propose:”