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Interpreting Adversarial Trained Convolutional Neural Networks Tianyuan Zhang , Zhanxing Zhu Peking University 1600012888@pku.edu.cn zhanxing.zhu@pku.edu.cn Poster: Pacific Ballroom #148 1 Contents Normally trained CNNs typically

  1. Interpreting Adversarial Trained Convolutional Neural Networks Tianyuan Zhang , Zhanxing Zhu Peking University 1600012888@pku.edu.cn zhanxing.zhu@pku.edu.cn Poster: Pacific Ballroom #148 � 1

  2. Contents • Normally trained CNNs typically lack of interpretability • Biased towards textures • Hypothesis: Adversarially trained CNNs could improve interpretability • Capture more semantic features: shapes. • Systematic experiments to validate the hypothesis • Discussions � 2

  3. Normally Trained CNN • Interpreting normally trained CNN: texture bias Published as a conference paper at ICLR 2019 I MAGE N ET - TRAINED CNN S ARE BIASED TOWARDS TEXTURE ; INCREASING SHAPE BIAS IMPROVES ACCURACY AND ROBUSTNESS Robert Geirhos Patricia Rubisch University of T¨ ubingen & IMPRS-IS University of T¨ ubingen & U. of Edinburgh robert.geirhos@bethgelab.org p.rubisch@sms.ed.ac.uk Claudio Michaelis Matthias Bethge ∗ University of T¨ ubingen & IMPRS-IS University of T¨ ubingen claudio.michaelis@bethgelab.org matthias.bethge@bethgelab.org Felix A. Wichmann ∗ Wieland Brendel ∗ University of T¨ ubingen University of T¨ ubingen felix.wichmann@uni-tuebingen.de wieland.brendel@bethgelab.org (a) Texture image (b) Content image (c) Texture-shape cue conflict 81.4% Indian elephant 71.1% tabby cat 63.9% Indian elephant 10.3% indri 17.3% grey fox 26.4% indri � 3 8.2% 3.3% 9.6% black swan Siamese cat black swan

  4. Fraction of 'shape' decisions Fraction of 'shape' decisions 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Shape categories Shape categories ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Fraction of 'texture' decisions Fraction of 'texture' decisions Augmented Stylized- ImageNet 
 could improve shape bias. � 4

  5. Are there any other models that could improve shape bias? Adversarially trained CNNs! � 5

  6. Adversarial Examples • Deep neural networks are easily fooled by adversarial examples. Not robust! f(x;w*) P(“panda”) = 57.7% f(x;w*) P(“gibbon”) = 99.3% ?! � 6

  7. 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AT-CNNs learned to make them robust? • Compared with standard CNNs, AT-CNNs tend to be more shape-biased. � 7

  8. Two ways for interpreting AT-CNNs • Qualitative method (Lots of people did this) • Visualizing sensitivity maps � 8

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