CONVOLUTIONAL NEURAL NETWORK SCALING METHODS IN SEMANTIC SEGMENTATION - Hmyria, I. O.; Kravets, N. S. (Radio Electronics, Computer Science, Control, 2024)
Context. Designing a new architecture is difficult and time-consuming process, that in some cases can be replaced by scaling existing model. In this paper we examine convolutional neural network scaling methods and aiming on the development of the method that allows to scale original network that solves segmentation task into more accurate network. Objective. The goal of the work is to develop a method of scaling a convolutional neural network, that achieve or outperform existing scaling methods, and to verify its effectiveness in solving semantic segmentation task. Method. The proposed asymmetric method combines advantages of other methods and provides same high accuracy network in the result as combined method and even outperform other methods. The method is developed to be appliable for convolutional neural networks which follows encoder-decoder architecture designed to solve semantic segmentation task. The method is enhancing feature extraction potential of the encoder part, meanwhile preserving decoder part of architecture. Because of its asymmetric nature, proposed method more efficient, since it results in smaller increase of parameters amount. Results. The proposed method was implemented on U-net architecture that was applied to solve semantic segmentation task. The evaluation of the method as well as other methods was performed on the semantic dataset. The asymmetric scaling method showed its efficiency outperformed or achieved other scaling methods results, meanwhile it has fewer parameters. Conclusions. Scaling techniques could be beneficial in cases where some extra computational resources are available. The proposed method was evaluated on the solving semantic segmentation task, on which method showed its efficiency. Even though scaling methods improves original network accuracy they highly increase network requirements, which proposed asymmetric method dedicated to decrease. The prospects for further research may include the optimization process and investigation of tradeoff between accuracy gain and resources requirements, as well as a conducting experiment that includes several different architectures.