Learning by Passing Tests, with Application to Neural Architecture Search
This work offers a new paradigm for machine learning, potentially improving model generalization and efficiency for researchers and practitioners in AI, by applying a human-inspired learning strategy.
This paper introduces Learning by Passing Tests (LPT), a novel learning approach where a 'tester' model generates increasingly difficult tests for a 'learner' model. The learner continuously improves to pass these tests, and when applied to neural architecture search, LPT significantly outperforms state-of-the-art baselines on CIFAR-10, CIFAR-100, and ImageNet.
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.