Unsupervised learning and continual learning in neural networks - Ji, X (2021)
For decades research has pursued the ambitious goal of designing computer models that learn to solve problems as effectively as humans can. Artificial neural networks -- generic, optimizable models that were originally inspired by biological neurons in the brain -- appear to provide a promising answer. However, a significant limitation of current models is that they tend to only be reliably proficient at tasks and datasets that they were explicitly trained on. If more than one task or dataset is being trained on, samples need to be appropriately mixed and balanced so that training on successive batches does not induce forgetting of knowledge learned in previous batches, which is an impediment to continual learning. Furthermore, associations need to be made explicit via paired input-target samples for the trained network to achieve its best performance on desired tasks; when the network is trained in an unsupervised manner without explicit targets, in an effort to reduce the cost of data collection, knowledge learned by the network transfers to desired tasks significantly worse compared to supervised training with explicit associations.