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.
翻译:通过测试进行学习是人类学习中广泛使用的一种方法,在改善学习结果方面显示出极大的效力:一系列测试的顺序越来越困难;学习者通过这些测试来确定其学习中的弱点,并不断解决这些弱点,以便成功地通过这些测试。我们有兴趣调查是否可以从人类那里借用这种强大的学习技术来提高机器的学习能力。我们建议一种叫通过测试进行学习的新颖的学习方法。在我们的方法中,一个测试者模型创造了越来越困难的测试来评价学习者模型。学习者试图不断提高其学习能力,以便成功地通过测试者所创造的无论多么困难的测试。我们建议了一个多层次的优化框架来制定LPT,让测试者学习者学会创造困难和有意义的测试,学习者学会通过这些测试。我们开发了一种有效的算法来解决LCT问题。我们的方法用于神经结构的搜索,并大大改进了CFAR-100、CIFAR-10和图像网络上最先进的基线。