Measuring the effect of nuisance variables on classifiers
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[1] Thomas Brox,et al. Inverting Visual Representations with Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[3] Seyed-Mohsen Moosavi-Dezfooli,et al. DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[5] Andrew Zisserman,et al. Deep Face Recognition , 2015, BMVC.
[6] N. Metropolis,et al. Equation of State Calculations by Fast Computing Machines , 1953, Resonance.
[7] John W. Fisher,et al. Highly-Expressive Spaces of Well-Behaved Transformations: Keeping it Simple , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[8] Ahmed M. Elgammal,et al. Digging Deep into the Layers of CNNs: In Search of How CNNs Achieve View Invariance , 2015, ICLR.
[9] Luca Rigazio,et al. Towards Deep Neural Network Architectures Robust to Adversarial Examples , 2014, ICLR.
[10] David L. Donoho,et al. Image Manifolds which are Isometric to Euclidean Space , 2005, Journal of Mathematical Imaging and Vision.
[11] Stefano Soatto,et al. An Empirical Evaluation of Current Convolutional Architectures’ Ability to Manage Nuisance Location and Scale Variability , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Pascal Frossard,et al. Manitest: Are classifiers really invariant? , 2015, BMVC.
[13] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[14] Stéphane Mallat,et al. Group Invariant Scattering , 2011, ArXiv.
[15] Andrea Vedaldi,et al. Understanding deep image representations by inverting them , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Pascal Frossard,et al. Analysis of classifiers’ robustness to adversarial perturbations , 2015, Machine Learning.
[17] Andrea Vedaldi,et al. Understanding Image Representations by Measuring Their Equivariance and Equivalence , 2014, International Journal of Computer Vision.
[18] Andrew Zisserman,et al. Spatial Transformer Networks , 2015, NIPS.
[19] Andrew Zisserman,et al. Return of the Devil in the Details: Delving Deep into Convolutional Nets , 2014, BMVC.
[20] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Stefano Soatto,et al. Visual Representations: Defining Properties and Deep Approximations , 2014, ICLR 2016.