Deep metric learning using Triplet network No Fuss Distance Metric Learning using Proxies

Another commonly used family of losses is triplet loss, which is defined by a triplet of data points: an anchor point, and a similar and dissimilar data points. The goal in a triplet loss is to learn a distance with respect to which the anchor point is closer to the similar point than to the dissimilar one.

Tri-training leverages three classifiers equally to give pseudo-labels to unlabeled samples, but the method does not assume labeling samples generated from a different domain. In this paper, we propose an asymmetric tri-training method for unsupervised domain adaptation, where we assign pseudo-labels to unlabeled samples and train neural networks as if they are true labels. In our work, we use three networks asymmetrically. By asymmetric, we mean that two networks are used to label unlabeled target samples and one network is trained by the samples to obtain target discriminative representations. Triplet Loss and Online Triplet Mining in TensorFlow