  Cross-dataset transfer learning is an important problem in person
re-identification (Re-ID). Unfortunately, not too many deep transfer Re-ID
models exist for realistic settings of practical Re-ID systems. We propose a
purely deep transfer Re-ID model consisting of a deep convolutional neural
network and an autoencoder. The latent code is divided into metric embedding
and nuisance variables. We then utilize an unsupervised training method that
does not rely on co-training with non-deep models. Our experiments show
improvements over both the baseline and competitors' transfer learning models.
