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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)
We suggest a loss for learning deep embeddings. The new loss does not introduce parameters that need to be tuned and results in very good embeddings across a range of datasets and problems. The loss is computed by estimating two distribution of similarities for positive (matching) and negative (non-matching) sample pairs, and then computing the probability… (More)
Review of various reduced-complexity min-sum based decoding algorithms for LDPC codes are presented. New BP-based approach for LDPC decoding was proposed, and one new a-min∗-min decoding algorithm was presented. Complexity, effectiveness, and average number of iteration for each algorithm are shown.