Evolutionary Multitasking Collaborative Neural Architecture Search for Scene Classification

With the acquisition of large-scale remote sensing data and the development of deep learning, convolutional neural networks have achieved great progress in scene clas-sification tasks. However, the current networks greatly rely on the experience design of experts, and a single network cannot cope with multiple complex scene categories. In this paper, we design a novel evolutionary multitasking collaborative neural architecture search (EMCNAS) for remote sensing scene classification. EMCNAS mainly explores the similar features of different remote sensing scene classification tasks, and utilizes the uniformly encoded population to achieve implicit collaborative transfer. EM CNAS is able to adaptively determine the degree of exchange of genetic material based on the similarity of tasks in different scenarios, enabling more effective positive transfer. Compared with excellent manually designed neural networks and NAS peers, the proposed EMCNAS achieved competitive results on two remote sensing scene classification benchmark datasets UC Merced LandUse and NWPU-RESISC45 datasets. Ablation experiments also demonstrate the effectiveness of the adaptive collaborative transfer designed in EMCNAS.