IBM Uses Continual Learning to Avoid The Amnesia Problem in Neural Networks
Using continual learning might avoid the famous catastrophic forgetting problem in neural networks.
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I often joke that neural networks suffers from a continuous amnesia problem in the sense that they every time they are retrained they lost the knowledge accumulated in previous iterations. Building neural networks that can learn incrementally without forgetting is one of the existential challenges facing the current generation of deep learning solutions. Over a year ago, researchers from IBM published a paper proposing a method for continual learning proposing that allow the implementation of neural networks that can build…
