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The impressive generalization performance of modern neural networks isattributed in part to their ability to implicitly memorize complex trainingpatterns. Inspired by this, we explore a novel mechanism to improve modelgeneralization via explicit memorization. Specifically, we propose theresidual-memorization (ResMem) algorithm, a new method that augments anexisting prediction model (e.g. a neural network) by fitting the model'sresiduals with a $k$-nearest neighbor based regressor. The final prediction isthen the sum of the original model and the fitted residual regressor. Byconstruction, ResMem can explicitly memorize the training labels. Empirically,we show that ResMem consistently improves the test set generalization of theoriginal prediction model across various standard vision and natural languageprocessing benchmarks. Theoretically, we formulate a stylized linear regressionproblem and rigorously show that ResMem results in a more favorable test riskover the base predictor.

ResMem: Learn what you can and memorize the rest