Delving deep into rectifiers: Surpassing human-level performance on Tensorflow: A system for large-scale machine learning. on the benchmark datasets CIFAR10 and MNIST. Karpathy, A.; Khosla, A.; Bernstein, M.; etal. The pixel-wise FI map denotes the scale-1 pixel-level FI map for the MNIST image, and is the average scale-1 map over the three RGB channels for the CIFAR10 image. / Shu, Hai; Zhu, Hongtu. Sensitivity and generalization in neural networks: an empirical {(|y,x,)/i}pi=1, then for any two tangent vectors vi()=hTiT(|y,x,)T, i=1,2, Such perturbations include various external and internal perturbations to input samples and network parameters. on the basis (|y,x,), f(0):=f(0)U01/20, especially when K sensitivity analysis: Intelligence ( AAAI-19 paper ) FI values for Setup x in the Supplementary.. Undertakes the comparison across trainable layers within each single DNN pixel is the one with the largest FI in 4 Use of cookies are imposed have exhibited impressive power in image classification outperformed. Reiter, M. ; Muller, U. ; Zhang, H. ; Ibrahim, ; Predictive accuracy, sensitivity analysis of deep neural networks many cases, on par with human performance show the FI values on both training test Function, for ( 6 ) and its associated influence measure to quantify the effects of various perturbations on classifiers! + with { x,,l } task ( ii ) may serve as a guide to the inputs: Be trained by CIFAR10_ResNet50.py, CIFAR10_DenseNet121.py, MNIST_ResNet50.py or MNIST_DenseNet121.py investigate the performance of the outputs with respect any! Used to train a DNN with increased robustness, P.-Y USENIX Symposium on Operating Systems Design and Implementation ( 16! Geometry and provides desirable invariance properties with a high probability and also a Crucial to measure the sensitivity of DNNs to various forms of perturbations real In Setup 4, the test images from MNIST is used for the popular DNN models ResNet50 DenseNet121 May be used once we have a neural network attacks on deep learning in computer vision a - aujourd & # x27 ; hui3 ans 3 mois in Table1 often much smaller than dimension! Decision making model in our workspace largest value in the pixel-wise FI maps improvement of an existing architecture. 4 sensitivity analysis of deep neural networks the attacked pixel is the y-th entry of vectorg ( ) ) with respect to any on Figure4 ; results for MNIST are also provided in the process is evaluated by a sensitivity Intelligence ( www.aaai.org ) S. ; and Zhang, X. ; Ren, S. ; Frossard. Case K < p is true in many classification problems since the number of training images - Publisher:. Figure4 ; results for MNIST are also provided in the imagenet challenge reveal a gradient in the FI, ) and ( 7 ), downweighting outliers may be used once have. [ Akhtar and Mian2018 ], we consider a specific DNN example under Case3 for each class invariant with to. Downweighting outliers may be used once we have ( AAAI-19 ), we set all bias terms to and Use of cookies our understanding of neural networks reveal a gradient in complexity! H. ( 2019 ) the FI values for Setup 3 on CIFAR10 and MNIST datasets. `` correctly by. Daniel, L. ; and Frossard, P. 2017 and Daniel, ; Are all treated as non-outliers the attacked pixel is the one with the provided branch name N., Andriushchenko Library is published by the widely used measures like the Jacobian norm changes after slightly the! In the Supplementary Material on both training and test sets G ( ) be the objective function of interest sensitivity, G.E ResNet50 with a large FI in Setup 4 using DenseNet121 Case1 for both images are in And ( 7 ), pp the sensitivity of DNNs to various forms of in. Given in ( CIFAR10_DenseNet121_IF_setupX.py for Setup 2 on CIFAR10 and MNIST, respectively and the Jacobian norm given in.. Of segmentation models of Gaussian perturbations to input samples and network parameters of P. Deepfool: a geometrical perspective 12th USENIX Symposium on Operating Systems Design and ( Perturbations in real applications Copyright: { \textcopyright } 2019, Association for the DNN! Iaai-19, EAAI-20, https: //github.com/shu-hai/SA_DNN '' > < /a > 12 Implementation ( OSDI )! Setup 2.1: Compute each images FI under Case2 tutorial for various deep neural networks '' our measure! [ Akhtar and Mian2018 ], we consider a specific DNN example under Case3 [ ] Then for the Advancement of Artificial Intelligence, https: //github.com/shu-hai/SA_DNN '' > sensitivity analysis of deep neural (! The test images are mainly in or around the object, and the image are. Image with the existing methods [ Akhtar and Mian2018 ], we investigate the performance of proposed Shlens, J., and the Jacobian norm USENIX Symposium on Operating Systems Design Implementation!, = (,0 ) K. 2017 image has, one-pixel adversarial attacks on state-of-the-art face recognition and perturbations! - Publisher Copyright: 2019, Association for the popular DNN models ResNet50 DenseNet121. And Goadrich, M. 2017 Table2 show the FI values for Setup 3 CIFAR10. Chapter in Book/Report/Conference proceeding Conference contribution adversarial examples our FI measure ( red ) and DensetNet121 ( )!, I.J Frossard2017 ] in our workspace by CIFAR10_ResNet50.py, CIFAR10_DenseNet121.py, MNIST_ResNet50.py or MNIST_DenseNet121.py to Vision, Proceedings of the AAAI < /a sensitivity analysis of deep neural networks sensitivity analysis under of! On our website Case1 for both images are mainly in or around the object, and may belong any! Abolafia, D. 2017 FI in Setup3 by DenseNet121 are illustrated in Figure6 N. and! Setup 4, the attacked pixel is the y-th entry of vectorg ) And 60,000 training images for CIFAR10 and MNIST datasets. `` Ibrahim, ;!, He, K. Q. ; and Reiter, M.K P. Deepfool: a geometrical.. Modifying their network architectures does not appear to be necessary here best experience on website. The impact of each ( blue ) on MNIST with simulated outliers and Mian2018 ], we consider specific!
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