FIVE-MINUTE NEURAL ARCHITEC-TURE SEARCH FOR IMAGE CLASSIFICATION, OBJECT-DETECTION, AND SUPER-RESOLUTION

Abstract

Neural network models have become more sophisticated with the explosive development of AI and its applications. Automating the model search process is essential to explore a full range of neural architectures for satisfactory performance. However, most current NAS algorithms consume significant time and computing resources, and many cater only to image classification applications. This paper proposes the total path count (TPC) score, which requires only simple calculation based on the architecture information, as an efficient accuracy predictor. TPC score is not only simple to come by but also very effective. The Kendall rank correlation coefficient of the TPC scores and the accuracies of 20 architectures for the CIFAR100 problem is as high as 0.87. This paper also proposes TPC-NAS, a zero-shot NAS method leveraging the novel TPC score. TPC-NAS requires no training and inference, and can complete a NAS task for Imagenet and other vision applications in less than five CPU minutes. Then, we apply TPC-NAS to image classification, object detection, and super-resolution applications for further validation. In image classification, TPC-NAS finds an architecture that achieves 76.4% top-1 accuracy in ImageNet with 355M FLOPs, outperforming other NAS solutions. Starting with yolov4-p5, TPC-NAS comes up with a highperformance architecture with at least 2% mAP improvement over other NAS algorithms' results in object detection. Finally, in the super-resolution application, TPC-NAS discovers an architecture with fewer than 300K parameters and generates images with 32.09dB PSNR in the Urban100 dataset. These three experiments convince us that the TPC-NAS method can swiftly deliver high-quality CNN architectures in diverse applications. The related source

1. INTRODUCTION

The complexity of high-performance machine learning models has skyrocketed, and manual tuning of hyperparameters and neural network (NN) architecture has become laborious and timeconsuming. More efficient methodologies for the design, training, and deployment of NN models are required. Toward this end, recently, we have witnessed rapid growth in research on neural (network) architecture search (NAS) that automates the model search process. Early NAS algorithms use evolutionary search (Real et al., 2017; 2019) or reinforcement learning (Zoph & Le, 2017; Tan et al., 2019) . However, such methods typically require multiple training of different architectures, which consumes significant computational resources and time. To reduce search time, differentiable NAS employs gradient descent (Mei et al., 2020; Chen et al., 2021b; Xu et al., 2020) that decides which architectures to keep by updating the weights between different operations. DARTS (Liu et al., 2019) , for example, makes the search space continuous by applying a softmax function to all possible operations. After training, only the operations with the highest softmax output will be retained as the final searched model. Later, Wang et al. (2021c) discovered that deciding on the final model based on its contribution to supernet performance outperforms deciding on the final model solely on the softmax output between architectures. Although gradientbased algorithms speed up the search process, they require the construction of a supernet that can

availability

code is available at https://github.com/TPC-NAS/TPC.

