@article{Chung_Fieguth_Wong_2020, title={Assessing Architectural Similarity in Populations of Deep Neural Networks}, volume={5}, url={https://openjournals.uwaterloo.ca/index.php/vsl/article/view/1668}, abstractNote={<p>Evolutionary deep intelligence has recently shown great promise<br>for producing small, powerful deep neural network models via the<br>synthesis of increasingly efficient architectures over successive<br>Gen No. Gene Tagging No Gene Tagging generations. However, little has been done to directly assess architectural similarity between networks during the synthesis process.<br>We present a preliminary study into quantifying architectural similarity via the percentage overlap of architectural clusters. Results<br>show that networks synthesized using architectural alignment (via<br>gene tagging) maintain higher architectural similarities within each<br>generation, potentially restricting the search space of highly efficient<br>network architectures.</p>}, number={1}, journal={Journal of Computational Vision and Imaging Systems}, author={Chung, Audrey G. and Fieguth, Paul and Wong, Alexander}, year={2020}, month={Jan.}, pages={1} }