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When training neural networks for classification tasks with backpropagation,parameters are updated on every trial, even if the sample is classifiedcorrectly. In contrast, humans concentrate their learning effort on errors.Inspired by human learning, we introduce lazy learning, which only learns onincorrect samples. Lazy learning can be implemented in a few lines of code andrequires no hyperparameter tuning. Lazy learning achieves state-of-the-artperformance and is particularly suited when datasets are large. For instance,it reaches 99.2% test accuracy on Extended MNIST using a single-layer MLP, anddoes so 7.6x faster than a matched backprop network

Lazy learning: a biologically-inspired plasticity rule for fast and energy efficient synaptic plasticity