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Open Set Domain Adaptation Methods for Classification Tasks

Explore open set domain adaptation techniques for classification tasks, adapting to unknown categories. Evaluate closed set datasets in an open set protocol, using iterative unsupervised methods. Experiment on various domain shifts and assess impact of unknowns in office dataset. Visit the poster session for more! (ID: 1)

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Open Set Domain Adaptation Methods for Classification Tasks

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  1. Pau Panareda Busto1,2Juergen Gall1University of Bonn1, Airbus Group Innovations2ICCV’17, 24th October 2017 – Venice, Italy OPEN SET Domain ADAPTATION

  2. Saenkoet al., Adapting Visual Category Models to New Domains (2010)1 Tommasi et al., A TestbedforCross-dataset Analysis (2014)2 (closed set) domain adaptation • Domain adaptation methods for classification tasks are tested on closed set data1,2  all samples belong to known object categories Training data / Source domain Test data / Target domain Panareda Busto & Gall, ICCV 17

  3. Scheirer et al., Towards Open Set Recognition (2013)1 Open set domain adaptation • More realistic scenario: Open set1 domain adaptation  additional instances belong to unknown categories, i.e. unlabelled Training data / Source domain Test data / Target domain Panareda Busto & Gall, ICCV 17

  4. Saenkoet al., Adapting Visual Category Models to New Domains (2010)1 From closed to open set protocol • Popular closed set datasets evaluated in an open set protocol: • Define a set of shared classes between source and target domains • Remaining categories are distributed between domains and set as unknown • Example: Office dataset1 (31 classes  10 shared + unknown) Training data / Source domain Test data / Target domain Panareda Busto & Gall, ICCV 17

  5. Assign-and-transform-iteratIvely (I) • Unsupervised domain adaptation technique for open set classification tasks:  Find linear mapping W from source to target domain 1) Assign source classes to target samples () or declare target samples as unknown () Source Target Assigned Target Panareda Busto & Gall, ICCV 17 Costofoutliers:

  6. Svanberg, A class of globally convergent optimization methods basedon conservativeconvexseparablea pproximations(2002)1 Assign-and-transform-iteratIvely (II) • Unsupervised domain adaptation technique for open set classification tasks:  Find linear mapping W from source to target domain 2) Optimise and update W Back to 1 until convergence 3) Label all target data with Linear SVM • Formulation naturally extends to a semi-supervised approach (poster session) Source Transformed Source Assigned Target Target Panareda Busto & Gall, ICCV 17

  7. Experiments – office dataset (I) • Office dataset (6 domain shifts – Amazon, DSLR, Webcam) vs. non-CNN based methods “5 random splits” vs. CNN based methods “all source samples” CS: Closed set (10 classes) OS*: Open set (10 classes) OS: Open set (10 + unknown) Long et al. (2015)1 Long et al. (2016)2 Ganin & Lempitsky(2015)3 Pan et al. (2009)4 Long et al. (2016)5 Gong et al. (2012)6 Sun et al. (2015)7 Panareda Busto & Gall, ICCV 17

  8. Sun et al., Return offrustratingly easy domainadaptation(2015)1 Experiments – IMPACT OF UNKNOWNs • Office dataset: A  D+W (5 random splits) LSVM: 54.1% it 1: 71.5% it 2: 77.8% it 3: 80.2% Panareda Busto & Gall, ICCV 17

  9. Thank you for your attention See you in the poster session! (ID: 1)

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