  The superiority of deeply learned pedestrian representations has been
reported in very recent literature of person re-identification (re-ID). In this
paper, we consider the more pragmatic issue of learning a deep feature with no
or only a few labels. We propose a progressive unsupervised learning (PUL)
method to transfer pretrained deep representations to unseen domains. Our
method is easy to implement and can be viewed as an effective baseline for
unsupervised re-ID feature learning. Specifically, PUL iterates between 1)
pedestrian clustering and 2) fine-tuning of the convolutional neural network
(CNN) to improve the original model trained on the irrelevant labeled dataset.
Since the clustering results can be very noisy, we add a selection operation
between the clustering and fine-tuning. At the beginning when the model is
weak, CNN is fine-tuned on a small amount of reliable examples which locate
near to cluster centroids in the feature space. As the model becomes stronger
in subsequent iterations, more images are being adaptively selected as CNN
training samples. Progressively, pedestrian clustering and the CNN model are
improved simultaneously until algorithm convergence. This process is naturally
formulated as self-paced learning. We then point out promising directions that
may lead to further improvement. Extensive experiments on three large-scale
re-ID datasets demonstrate that PUL outputs discriminative features that
improve the re-ID accuracy.
