  Our paper addresses the problem of models struggling to learn diverse
features, due to either forgetting previously learned features or failing to
learn new ones. To overcome this problem, we introduce Diverse Feature Learning
(DFL), a method that combines an important feature preservation algorithm with
a new feature learning algorithm. Specifically, for preserving important
features, we utilize self-distillation in ensemble models by selecting the
meaningful model weights observed during training. For learning new features,
we employ reset that involves periodically re-initializing part of the model.
As a result, through experiments with various models on the image
classification, we have identified the potential for synergistic effects
between self-distillation and reset.
