Print Email Facebook Twitter Unsupervised Cross Domain Image Matching with Outlier Detection Title Unsupervised Cross Domain Image Matching with Outlier Detection Author Liu, Xin (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems) Contributor van Gemert, J.C. (mentor) Khademi, S. (mentor) Reinders, M.J.T. (graduation committee) Verwer, S.E. (graduation committee) Degree granting institution Delft University of Technology Date 2018-08-31 Abstract This work proposes a method for matching images from different domains in an unsupervised manner, and detecting outlier samples in the target domain at the same time. This matching problem is made difficult by i) the different domain images that are related but under different conditions (e.g. photos of the same location captured in different illuminations), ii) unsupervised settings with paired-image information available only for one of the domains, iii) the existingof outliers that makes the two domains not fully overlap. To this end, we propose an end-to-end architecture that can match cross domain images in an unsupervised manner and handle not fully overlapping domains by outlierdetection. Our architecture is composed of three subnetworks, two of which are fed with pairs of source images to learn the ”match” information. The other subnetwork is fed with target images, and works together with the other two subnetworks to learn domain invariant representations of the source samples and the target inlier samples by applying a weighted multi-kernel Maximum Mean Discrepancy (weighted MK-MMD). We propose the weighted MK-MMD, together with an entropy loss, for outlier detection. The entropy loss iteratively outputs the probability of a target sample to be an inlier during training. And the probabilities are used as weights in our weighted MK-MMD for aligning only the target inlier samples with the source samples. Extensive experimental evidence on Office [26] dataset and our proposed datasets Shape, Pitts-CycleGAN shows that the proposed approach yields state-of-the-art cross domain image matching and outlier detection performance on different benchmarks. Subject Computer VisionDomain AdaptationImage MatchingOutlier Detection To reference this document use: http://resolver.tudelft.nl/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73 Part of collection Student theses Document type master thesis Rights © 2018 Xin Liu Files PDF Master_Thesis_Xin_Liu.pdf 5.11 MB Close viewer /islandora/object/uuid:fcd6c0f8-6618-4fdb-b8ad-e183b3a81b73/datastream/OBJ/view