智能科学与技术学报 (Jun 2024)
Neural architecture search for 3D model classification based on adaptive smoothness strategy
Abstract
Aiming at the problem of poor generalization ability in hand-crafted architectures that overly rely on expert experience, a neural network architecture search method with an adaptive smoothness strategy was proposed. Firstly, an improved candidate operation selection strategy and a continuous relaxation method were used to convert discrete search space into continuous space, and a weight-sharing mechanism was employed to enhance search efficiency. Secondly, a regularization operation with an adaptive smoothness strategy was added to the loss function, whose smoothness degree was controlled by a temperature parameter. Finally, the loss function was calculated using an exponential normalization method to avoid loss value overflow. Experimental results on 3D point cloud datasets and protein-protein interaction datasets showed that the proposed method achieved higher classification accuracy and more stable performance under the same training samples and iterations.
