Learning by Passing Tests, with Application to Neural Architecture Search

TL;DR

This paper introduces a novel learning framework inspired by human test-based learning, where a tester creates increasingly difficult tests to improve a machine learner's abilities, applied here to neural architecture search with strong results.

Contribution

The paper proposes a new multi-level optimization framework called learning by passing tests (LPT), integrating test difficulty progression into neural architecture search.

Findings

Achieves significant improvements over state-of-the-art on CIFAR-100, CIFAR-10, and ImageNet.

Develops an efficient algorithm for the LPT optimization problem.

Demonstrates the effectiveness of test-based learning in machine learning contexts.

Abstract

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.