IRLAS: Inverse Reinforcement Learning for Architecture Search
This work addresses the challenge of designing efficient neural architectures for computer vision, offering a novel approach that improves speed and accuracy, though it is incremental in leveraging existing search algorithms.
The paper tackles the problem of neural architecture search by proposing an inverse reinforcement learning method that learns topological knowledge from human-designed networks to guide the search, resulting in architectures that achieve a 2.60% error rate on CIFAR-10 and a top-1 accuracy of 75.28% on ImageNet with 2-4x faster inference than most auto-generated architectures.
In this paper, we propose an inverse reinforcement learning method for architecture search (IRLAS), which trains an agent to learn to search network structures that are topologically inspired by human-designed network. Most existing architecture search approaches totally neglect the topological characteristics of architectures, which results in complicated architecture with a high inference latency. Motivated by the fact that human-designed networks are elegant in topology with a fast inference speed, we propose a mirror stimuli function inspired by biological cognition theory to extract the abstract topological knowledge of an expert human-design network (ResNeXt). To avoid raising a too strong prior over the search space, we introduce inverse reinforcement learning to train the mirror stimuli function and exploit it as a heuristic guidance for architecture search, easily generalized to different architecture search algorithms. On CIFAR-10, the best architecture searched by our proposed IRLAS achieves 2.60% error rate. For ImageNet mobile setting, our model achieves a state-of-the-art top-1 accuracy 75.28%, while being 2~4x faster than most auto-generated architectures. A fast version of this model achieves 10% faster than MobileNetV2, while maintaining a higher accuracy.