DQNAS: Neural Architecture Search using Reinforcement Learning
This work addresses the need for efficient neural architecture search to automate network design, reducing computational resource consumption and expert effort, though it appears incremental as it builds on existing reinforcement learning and one-shot training methods.
The paper tackles the problem of designing convolutional neural networks, which is labor-intensive and requires expert knowledge, by proposing DQNAS, an automated neural architecture search framework using reinforcement learning and one-shot training to generate architectures with superior performance and reduced scalability issues.
Convolutional Neural Networks have been used in a variety of image related applications after their rise in popularity due to ImageNet competition. Convolutional Neural Networks have shown remarkable results in applications including face recognition, moving target detection and tracking, classification of food based on the calorie content and many more. Designing of Convolutional Neural Networks requires experts having a cross domain knowledge and it is laborious, which requires a lot of time for testing different values for different hyperparameter along with the consideration of different configurations of existing architectures. Neural Architecture Search is an automated way of generating Neural Network architectures which saves researchers from all the brute-force testing trouble, but with the drawback of consuming a lot of computational resources for a prolonged period. In this paper, we propose an automated Neural Architecture Search framework DQNAS, guided by the principles of Reinforcement Learning along with One-shot Training which aims to generate neural network architectures that show superior performance and have minimum scalability problem.