Before revision:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on  RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB  .
After revision:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB . Results show that our models seem to generalize well on new data and are learned to focus on relevant facial representations for expressions recognition .