Learning through tests is a broadly used methodology in human learning and shows great effectiveness in improving learning outcome: a sequence of tests are made with increasing levels of difficulty; the learner takes these tests to identify his/her weak points in learning and continuously addresses these weak points to successfully pass these tests. We are interested in investigating whether this powerful learning technique can be borrowed from humans to improve the learning abilities of machines. We propose a novel learning approach called learning by passing tests (LPT). In our approach, a tester model creates increasingly more-difficult tests to evaluate a learner model. The learner tries to continuously improve its learning ability so that it can successfully pass however difficult tests created by the tester. We propose a multi-level optimization framework to formulate LPT, where the tester learns to create difficult and meaningful tests and the learner learns to pass these tests. We develop an efficient algorithm to solve the LCT problem. Our method is applied for neural architecture search and achieves significant improvement over state-of-the-art baselines on CIFAR-100, CIFAR-10, and ImageNet.

中文翻译：

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通过测试学习，并应用于神经体系结构搜索

通过测试进行学习是人类学习中广泛使用的方法，并且在改善学习成果方面显示出巨大的效果。学习者将通过这些测试来确定自己的学习薄弱点，并不断解决这些薄弱点，以成功通过这些测试。我们有兴趣研究这种强大的学习技术是否可以从人身上借鉴来提高机器的学习能力。我们提出了一种新颖的学习方法，称为通过考试学习（LPT）。在我们的方法中，测试者模型创建的难度越来越大，以评估学习者模型。学习者试图不断提高其学习能力，以使其能够成功地通过测试人员创建的困难测试。我们提出了一个多层次的优化框架来制定LPT，在该框架中，测试人员将学习创建困难且有意义的测试，而学习人员则将学习通过这些测试。我们开发了一种有效的算法来解决LCT问题。我们的方法适用于神经体系结构搜索，并且相对于CIFAR-100，CIFAR-10和ImageNet的最新基准具有显着改进。