An evolutionary search algorithm for efficient ResNet-based architectures: a case study on gender recognition

Neural Architecture Search (NAS) is a busy research field growing exponentially in recent years. State-of-the-art deep neural networks usually require a specialist to fine-tune the model to solve a specific problem. NAS research aims to design neural network architectures automatically, thus easing the need for machine learning specialists to spend a lot of effort on hand-crafted attempts. As artificial intelligence applications are becoming ubiquitous, there is also a growing interest in efficient applications that could be deployed to smartphones, smart wearable devices, and other edge devices. Gender recognition in unfiltered images — such as those we find in real-world situations like pictures taken with smartphones and video shots from surveillance cameras — is one of such challenging applications. In this work, we developed an evolutionary NAS algorithm that consistently finds efficient ResNet-based architectures, named RENNAS, which have a good trade-off between classification accuracy and architectural and computational complexities. We demonstrate our algorithm’s performance on Adience dataset of unfiltered images for gender recognition.

Recommended citation: A. R. F. Da Silva, L. M. Pavelski, L. A. Q. Cordovil Junior, P. H. De Oliveira Gomes, L. M. Azevedo, and F. E. Fernandes Jr, “An evolutionary search algorithm for efficient ResNet-based architectures: a case study on gender recognition,” in 2022 IEEE Congress on Evolutionary Computation (CEC), Padua, Italy: IEEE, Jul. 2022, pp. 1–10. doi: 10.1109/CEC55065.2022.9870434.
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