  Mutual learning is an ensemble training strategy to improve generalization by
transferring individual knowledge to each other while simultaneously training
multiple models. In this work, we propose an effective mutual learning method
for deep metric learning, called Diversified Mutual Metric Learning, which
enhances embedding models with diversified mutual learning. We transfer
relational knowledge for deep metric learning by leveraging three kinds of
diversities in mutual learning: (1) model diversity from different
initializations of models, (2) temporal diversity from different frequencies of
parameter update, and (3) view diversity from different augmentations of
inputs. Our method is particularly adequate for inductive transfer learning at
the lack of large-scale data, where the embedding model is initialized with a
pretrained model and then fine-tuned on a target dataset. Extensive experiments
show that our method significantly improves individual models as well as their
ensemble. Finally, the proposed method with a conventional triplet loss
achieves the state-of-the-art performance of Recall@1 on standard datasets:
69.9 on CUB-200-2011 and 89.1 on CARS-196.
