  Deep representation learning using triplet network for classification suffers
from a lack of theoretical foundation and difficulty in tuning both the network
and classifiers for performance. To address the problem, local-margin triplet
loss along with local positive and negative mining strategy is proposed with
theory on how the strategy integrate nearest-neighbor hyper-parameter with
triplet learning to increase subsequent classification performance. Results in
experiments with 2 public datasets, MNIST and Cifar-10, and 2 small medical
image datasets demonstrate that proposed strategy outperforms end-to-end
softmax and typical triplet loss in settings without data augmentation while
maintaining utility of transferable feature for related tasks. The method
serves as a good performance baseline where end-to-end methods encounter
difficulties such as small sample data with limited allowable data
augmentation.
