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We propose to utilize gradients for detecting adversarial and out-of-distribution samples.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I., Shlens, J., and Szegedy, C · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., and Xiao, J · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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An overview of gradient descent optimization algorithms
Ruder, S · 2016
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2017
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On the limitation of convolutional neural networks in recognizing negative images
Hosseini, H., Xiao, B., Jaiswal, M., and Poovendran, R · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
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Adversarial examples in the physical world
Kurakin, A., Goodfellow, I., and Bengio, S · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
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Exploring generalization in deep learning
Neyshabur, B., Bhojanapalli, S., McAllester, D., and Srebro, N · 2017
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On the expressive power of deep neural networks
Raghu, M., Poole, B., Kleinberg, J., Ganguli, S., and Sohl-Dickstein, J · 2017
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
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Revisiting unreasonable effectiveness of data in deep learning era
Sun, C., Shrivastava, A., Singh, S., and Gupta, A · 2017
Cure-or: Challenging unreal and real environments for object recognition
Temel, D., Lee, J., and AlRegib, G · 2018
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Between-class learning for image classification
Tokozume, Y., Ushiku, Y., and Harada, T · 2018
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Learning a deep convnet for multi-label classification with partial labels
Durand, T., Mehrasa, N., and Mori, G · 2019
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Distorted representation space characterization through backpropagated gradients
Kwon, G., Prabhushankar, M., Temel, D., and AlRegib, G · 2019
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Application of deep learning to cybersecurity: A survey
Mahdavifar, S. and Ghorbani, A. A · 2019
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Object recognition under multifarious conditions: A reliability analysis and a feature similarity-based performance estimation
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Cure-tsr: Challenging unreal and real environments for traffic sign recognition
Temel, D., Kwon, G., Prabhushankar, M., and AlRegib, G · 2017
Cited alongside, same era.
Holographic feature representations of deep networks
Zinkevich, M. A., Davies, A., and Schuurmans, D · 2017
Cited alongside, same era.
Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V. N · 2018
Cited alongside, same era.
Learning confidence for out-of-distribution detection in neural networks
DeVries, T. and Taylor, G. W · 2018
Cited alongside, same era.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Lee, K., Lee, K., Lee, H., and Shin, J · 2018
Cited alongside, same era.
Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., and Srikant, R · 2018
Cited alongside, same era.
Temel, D., Lee, J., and AlRegib, G · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., and Yoo, Y · 2019
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Backpropagated gradient representations for anomaly detection
Kwon, G., Prabhushankar, M., Temel, D., and AlRegib, G · 2020
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Gradients as a measure of uncertainty in neural networks
Lee, J. and AlRegib, G · 2020
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Energy-based out-of-distribution detection
Liu, W., Wang, X., Owens, J., and Li, Y · 2020
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Contrastive explanations in neural networks
Prabhushankar, M., Kwon, G., Temel, D., and AlRegib, G · 2020
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Plm: Partial label masking for imbalanced multi-label classification
Duarte, K., Rawat, Y., and Shah, M · 2021
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Open-set recognition with gradient-based representations
Lee, J. and AlRegib, G · 2021
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Extracting causal visual features for limited label classification
Prabhushankar, M. and AlRegib, G · 2021
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