论文标题
通过通过测试进行学习，并应用于神经体系结构搜索
Learning by Passing Tests, with Application to Neural Architecture Search
论文作者
论文摘要
通过测试学习是人类学习中广泛使用的方法，在改善学习成果方面表现出很大的有效性：一系列测试是随着难度的增加而制造的；学习者接受这些测试来确定他/她的学习弱点，并不断地解决这些弱点以成功地通过这些测试。我们有兴趣调查这种强大的学习技术是否可以从人类那里借来以提高机器的学习能力。我们提出了一种通过通过测试（LPT）来称为学习的新颖学习方法。在我们的方法中，测试人员模型会创建越来越难以评估学习者模型的测试。学习者试图不断提高其学习能力，以便可以成功地通过测试人员创造的困难测试。我们提出了一个多级优化框架来制定LPT，测试人员学会了创建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LPT问题。我们的方法应用于神经架构搜索，并对CIFAR-100，CIFAR-10和Imagenet上的最新基准进行了显着改善。
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 LPT 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.
