  Ensembling deep learning models is a shortcut to promote its implementation
in new scenarios, which can avoid tuning neural networks, losses and training
algorithms from scratch. However, it is difficult to collect sufficient
accurate and diverse models through once training. This paper proposes
Auto-Ensemble (AE) to collect checkpoints of deep learning model and ensemble
them automatically by adaptive learning rate scheduling algorithm. The
advantage of this method is to make the model converge to various local optima
by scheduling the learning rate in once training. When the number of lo-cal
optimal solutions tends to be saturated, all the collected checkpoints are used
for ensemble. Our method is universal, it can be applied to various scenarios.
Experiment results on multiple datasets and neural networks demonstrate it is
effective and competitive, especially on few-shot learning. Besides, we
proposed a method to measure the distance among models. Then we can ensure the
accuracy and diversity of collected models.
