3D Model Classification Based on Neural Architecture Search

Differentiable architecture search algorithms are largely suffering from the well-known performance of discretization discrepancy,imbalances-update and aggregation of skip-connections.To weaken this impact,a novel approach named resource balance neural architecture search(RBNAS)is proposed.Firstly,the weak operators that contribute less to performance are gradually cut out by the resource-balanced method.Secondly,to make each operation have its own weight in the search process and contribute to the supernet,the relationship between the operations is changed to cooperation before competition.Finally,Gaussian noise is added into candidate operations to minimize the unfair competition of skip-connections.ModelNet and CIFAR-10 datasets are used to carry out a series of experiments,the results show that the algorithm can minimize discretization discrepancy and avoiding collapse caused by skip-connections.Compared with SGAS,NoisyDARTS,and manually designed network architectures,RBNAS can find a series of Pareto-optimal architectures efficiently and achieve a good performance in point cloud classification.