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OPEN SET Domain ADAPTATION

Pau Panareda Busto 1,2 Juergen Gall 1 University of Bonn 1 , Airbus Group Innovations 2 ICCV’17, 24th October 2017 – Venice, Italy. OPEN SET Domain ADAPTATION. Saenko et al., Adapting Visual C ategory Models to New Domains (2010) 1

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OPEN SET Domain ADAPTATION

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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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