Title: Training Triplet Networks with GAN,
Abstract: Triplet networks are widely used models that are characterized by good
performance in classification and retrieval tasks. In this work we propose to
train a triplet network by putting it as the discriminator in Generative
Adversarial Nets (GANs). We make use of the good capability of representation
learning of the discriminator to increase the predictive quality of the model.
We evaluated our approach on Cifar10 and MNIST datasets and observed
significant improvement on the classification performance using the simple k-nn
method.