Learn More
We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot(More)
In this work we explore the applicability of the recently proposed convolutional neural net architecture, called Bilinear CNN, and its new modification that we call Multiregion Bilinear CNN to the person re-identification problem. Originally, Bilinear CNNs were introduced for fine-grained classification and proved to be both simple and high-performing(More)
  • 1