Classifier-Agnostic Saliency Map Extraction

Extracting saliency maps, which indicate parts of the image important to classification, requires many tricks to achieve satisfactory performance when using classifier-dependent methods. Instead, we propose classifier-agnostic saliency map extraction. This allows to find all parts of the image that any classifier could use, not just one given in advance. This way we extract much higher quality saliency maps.

[1]  Michal Irani,et al.  “Double-DIP”: Unsupervised Image Decomposition via Coupled Deep-Image-Priors , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Joost van de Weijer,et al.  Context Proposals for Saliency Detection , 2018, Comput. Vis. Image Underst..

[3]  Qingquan Song,et al.  Towards Explanation of DNN-based Prediction with Guided Feature Inversion , 2018, KDD.

[4]  Nicolas Pugeault,et al.  Salient Region Segmentation , 2018, ArXiv.

[5]  Yarin Gal,et al.  Real Time Image Saliency for Black Box Classifiers , 2017, NIPS.

[6]  David M. Blei,et al.  Stochastic Gradient Descent as Approximate Bayesian Inference , 2017, J. Mach. Learn. Res..

[7]  Andrea Vedaldi,et al.  Interpretable Explanations of Black Boxes by Meaningful Perturbation , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[8]  Andrea Vedaldi,et al.  Salient Deconvolutional Networks , 2016, ECCV.

[9]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[10]  Zhe L. Lin,et al.  Top-Down Neural Attention by Excitation Backprop , 2016, International Journal of Computer Vision.

[11]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Wei Xu,et al.  Look and Think Twice: Capturing Top-Down Visual Attention with Feedback Convolutional Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[14]  Alexander Binder,et al.  On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation , 2015, PloS one.

[15]  Ivan Laptev,et al.  Is object localization for free? - Weakly-supervised learning with convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Thomas Brox,et al.  Striving for Simplicity: The All Convolutional Net , 2014, ICLR.

[17]  Ronan Collobert,et al.  From image-level to pixel-level labeling with Convolutional Networks , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[20]  Andrew Zisserman,et al.  Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps , 2013, ICLR.

[21]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[22]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[23]  Yee Whye Teh,et al.  Bayesian Learning via Stochastic Gradient Langevin Dynamics , 2011, ICML.

[24]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Alexandru Telea,et al.  An Image Inpainting Technique Based on the Fast Marching Method , 2004, J. Graphics, GPU, & Game Tools.

[26]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[27]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[28]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[29]  Jinsung Yoon,et al.  GENERATIVE ADVERSARIAL NETS , 2018 .

[30]  Lijie Fan,et al.  Adversarial Localization Network , 2017 .