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Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning models in an open-world setting.
Sung, K.K.: Learning and example selection for object and pattern detection. Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MA, USA (1995)
1995
Earlier work this paper cites.
Torralba, A., Fergus, R., Freeman, W.T.: 80 million tiny images: A large data set for nonparametric object and scene recognition. IEEE transactions on pattern analysis and machine intelligence 30
2008
Earlier work this paper cites.
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE transactions on pattern analysis and machine intelligence 32
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. JMLR 12
2011
Earlier work this paper cites.
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning (2011)
2011
Earlier work this paper cites.
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Šrndić, N., Laskov, P., Giacinto, G., Roli, F.: Evasion attacks against machine learning at test time. In: ECML PKDD. pp. 387–402. Springer (2013)
2013
Earlier work this paper cites.
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., , Vedaldi, A.: Describing textures in the wild. In: CVPR (2014)
2014
Earlier work this paper cites.
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: Intriguing properties of neural networks. ICLR (2014)
2014
Earlier work this paper cites.
Bendale, A., Boult, T.: Towards open world recognition. In: CVPR. pp. 1893–1902 (2015)
2015
Earlier work this paper cites.
Gidaris, S., Komodakis, N.: Object detection via a multi-region and semantic segmentation-aware cnn model. In: ICCV. pp. 1134–1142 (2015)
2015
Earlier work this paper cites.
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. ICLR (2015)
2015
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. ICLR (2015)
2015
Earlier work this paper cites.
Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., Moreno-Noguer, F.: Discriminative learning of deep convolutional feature point descriptors. In: ICCV. pp. 118–126 (2015)
2015
Earlier work this paper cites.
Wang, X., Gupta, A.: Unsupervised learning of visual representations using videos. In: ICCV. pp. 2794–2802 (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Becker, A., Ducas, L., Gama, N., Laarhoven, T.: New directions in nearest neighbor searching with applications to lattice sieving. In: Proceedings of the twenty-seventh annual ACM-SIAM symposium on Discrete algorithms. pp. 10–24. SIAM (2016)
2016
Earlier work this paper cites.
Cui, Y., Zhou, F., Lin, Y., Belongie, S.: Fine-grained categorization and dataset bootstrapping using deep metric learning with humans in the loop. In: CVPR. pp. 1153–1162 (2016)
2016
Earlier work this paper cites.
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: 2016 IEEE European symposium on security and privacy (EuroS&P). pp. 372–387. IEEE (2016)
2016
Earlier work this paper cites.
Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: CVPR. pp. 761–769 (2016)
2016
Cited alongside, same era.
Zagoruyko, S., Komodakis, N.: Wide residual networks. Proceedings of the British Machine Vision Conference (2016)
2016
Cited alongside, same era.
2017
Cited alongside, same era.
Harwood, B., Kumar BG, V., Carneiro, G., Reid, I., Drummond, T.: Smart mining for deep metric learning. In: ICCV. pp. 2821–2829 (2017)
2017
Cited alongside, same era.
Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. ICLR (2017)
2017
Duan, Y., Chen, L., Lu, J., Zhou, J.: Deep embedding learning with discriminative sampling policy. In: CVPR. pp. 4964–4973 (2019)
2019
Later among the works it cites.
Hein, M., Andriushchenko, M., Bitterwolf, J.: Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem. In: CVPR. pp. 41–50 (2019)
2019
Later among the works it cites.
Hendrycks, D., Dietterich, T.: Benchmarking neural network robustness to common corruptions and perturbations. ICLR (2019)
2019
Later among the works it cites.
Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. ICLR (2019)
2019
Later among the works it cites.
Laidlaw, C., Feizi, S.: Functional adversarial attacks. In: NeurIPS. pp. 10408–10418 (2019)
2019
Later among the works it cites.
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Cited alongside, same era.
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR. pp. 4700–4708 (2017)
2017
Cited alongside, same era.
Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: NeurIPS. pp. 6402–6413 (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Wu, C.Y., Manmatha, R., Smola, A.J., Krahenbuhl, P.: Sampling matters in deep embedding learning. In: ICCV. pp. 2840–2848 (2017)
2017
Cited alongside, same era.
Yuan, Y., Yang, K., Zhang, C.: Hard-aware deeply cascaded embedding. In: ICCV. pp. 814–823 (2017)
2017
Cited alongside, same era.
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: A 10 million image database for scene recognition. IEEE transactions on pattern analysis and machine intelligence 40
2017
Cited alongside, same era.
Athalye, A., Carlini, N., Wagner, D.: Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples. In: ICML. pp. 274–283. PMLR (2018)
2018
Cited alongside, same era.
Najafi, A., Maeda, S.i., Koyama, M., Miyato, T.: Robustness to adversarial perturbations in learning from incomplete data. In: NeurIPS. pp. 5541–5551 (2019)
2019
Later among the works it cites.
Sehwag, V., Bhagoji, A.N., Song, L., Sitawarin, C., Cullina, D., Chiang, M., Mittal, P.: Analyzing the robustness of open-world machine learning. In: Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security. pp. 105–116 (2019)
2019
Later among the works it cites.
Suh, Y., Han, B., Kim, W., Lee, K.M.: Stochastic class-based hard example mining for deep metric learning. In: CVPR. pp. 7251–7259 (2019)
2019
Later among the works it cites.
Uesato, J., Alayrac, J.B., Huang, P.S., Stanforth, R., Fawzi, A., Kohli, P.: Are labels required for improving adversarial robustness? NeurIPS (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Bitterwolf, J., Meinke, A., Hein, M.: Certifiably adversarially robust detection of out-of-distribution data. NeurIPS 33
2020
Closest in time.
Filos, A., Tigkas, P., McAllister, R., Rhinehart, N., Levine, S., Gal, Y.: Can autonomous vehicles identify, recover from, and adapt to distribution shifts? In: ICML. pp. 3145–3153. PMLR (2020)
2020
Closest in time.
Hsu, Y.C., Shen, Y., Jin, H., Kira, Z.: Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data. CVPR (2020)
2020
Closest in time.
Liu, W., Wang, X., Owens, J., Li, Y.: Energy-based out-of-distribution detection. NeurIPS (2020)
2020
Closest in time.
Meinke, A., Hein, M.: Towards neural networks that provably know when they don’t know. ICLR (2020)
2020
Closest in time.
Mohseni, S., Pitale, M., Yadawa, J., Wang, Z.: Self-supervised learning for generalizable out-of-distribution detection. In: AAAI. vol. 34, pp. 5216–5223 (2020)
2020
Closest in time.
Huang, R., Li, Y.: Towards scaling out-of-distribution detection for large semantic space. CVPR (2021)
2021
Closest in time.
Lin, Z., Dutta, S., Li, Y.: Mood: Multi-level out-of-distribution detection. CVPR (2021)
2021
Closest in time.
Papadopoulos, A., Rajati, M.R., Shaikh, N., Wang, J.: Outlier exposure with confidence control for out-of-distribution detection. Neurocomputing 441
2021
Closest in time.