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Learning by ignoring, which identifies less important things and excludesthem from the learning process, is broadly practiced in human learning and hasshown ubiquitous effectiveness. There has been psychological studies showingthat learning to ignore certain things is a powerful tool for helping peoplefocus. In this paper, we explore whether this useful human learning methodologycan be borrowed to improve machine learning. We propose a novel machinelearning framework referred to as learning by ignoring (LBI). Our frameworkautomatically identifies pretraining data examples that have large domain shiftfrom the target distribution by learning an ignoring variable for each exampleand excludes them from the pretraining process. We formulate LBI as athree-level optimization framework where three learning stages are involved:pretraining by minimizing the losses weighed by ignoring variables; finetuning;updating the ignoring variables by minimizing the validation loss. Agradient-based algorithm is developed to efficiently solve the three-leveloptimization problem in LBI. Experiments on various datasets demonstrate theeffectiveness of our framework.

Learning by Ignoring, with Application to Domain Adaptation