The 2 main factors that contribute to faster search are (1) ants search for architectures progressively (meaning that early architectures can be evaluated really fast), (2) ants can reuse the weights as they are associated with the graph.
All test were done using Google Colab. Even though results might not seem that impressive, I am still really excited to see what will happen when ants will be allowed to search for more complex architectures which use multi-branching.
Runtime compared to genetic architecture search (using similar settings): https://edvinasbyla.com/assets/images/devol-deepswarm-runtim...
The error rate on CIFAR-10, before the final training (meaning that topologies weren't fully trained and no augmentation was used): https://edvinasbyla.com/assets/images/ant-before-train.pdf
The error rate on CIFAR-10, before the final training compared to genetic architecture search (using similar settings): https://edvinasbyla.com/assets/images/devol-deepswarm-cifar....
The 2 main factors that contribute to faster search are (1) ants search for architectures progressively (meaning that early architectures can be evaluated really fast), (2) ants can reuse the weights as they are associated with the graph.
All test were done using Google Colab. Even though results might not seem that impressive, I am still really excited to see what will happen when ants will be allowed to search for more complex architectures which use multi-branching.