Fetching the paper…
Reading the bibliography…
Detecting out-of-distribution (OOD) inputs is a central challenge for safely deploying machine learning models in the real world.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
Earlier work this paper cites.
Xiao, J., Hays, J., Ehinger, K.A., Oliva, A., Torralba, A.: Sun database: Large-scale scene recognition from abbey to zoo. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 3485–3492. IEEE Computer Society (2010)
2010
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.
Ba, J., Frey, B.: Adaptive dropout for training deep neural networks. In: Advances in Neural Information Processing Systems. vol. 26 (2013)
2013
Earlier work this paper cites.
Wan, L., Zeiler, M.D., Zhang, S., LeCun, Y., Fergus, R.: Regularization of neural networks using dropconnect. In: Proceedings of the International Conference on Machine Learning. vol. 28, pp. 1058–1066 (2013)
2013
Earlier work this paper cites.
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3606–3613 (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. In: Journal of Machine Learning Research. vol. 15, pp. 1929–1958 (2014)
2014
Earlier work this paper cites.
Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: Proceedings of the Advances in Neural Information Processing Systems. vol. 28, pp. 1135–1143 (2015)
2015
Earlier work this paper cites.
Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 427–436 (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
Bendale, A., Boult, T.E.: Towards open set deep networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1563–1572 (2016)
2016
Earlier work this paper cites.
Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: Proceedings of the International Conference on Machine Learning. pp. 1050–1059 (2016)
2016
Earlier work this paper cites.
Han, S., Mao, H., Dally, W.J.: Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding. In: Proceedings of the International Conference on Learning Representations (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Proceedings of the European conference on computer vision. pp. 630–645. Springer (2016)
2016
Earlier work this paper cites.
Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. Proceedings of International Conference on Learning Representations (2017)
2017
Earlier work this paper cites.
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 4700–4708 (2017)
2017
Earlier work this paper cites.
Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in neural information processing systems. pp. 6402–6413 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Li, H., Kadav, A., Durdanovic, I., Samet, H., Graf, H.P.: Pruning filters for efficient convnets. In: Proceedings of International Conference on Learning Representations (2017)
2017
Earlier work this paper cites.
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2097–2106 (2017)
2017
Earlier work this paper cites.
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: A 10 million image database for scene recognition. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. vol. 40, pp. 1452–1464. IEEE (2017)
2017
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Lee, K., Lee, K., Lee, H., Shin, J.: A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In: Advances in Neural Information Processing Systems. pp. 7167–7177 (2018)
2018
Cited alongside, same era.
Tack, J., Mo, S., Jeong, J., Shin, J.: Csi: Novelty detection via contrastive learning on distributionally shifted instances. In: Advances in Neural Information Processing Systems (2020)
2020
Later among the works it cites.
Van Amersfoort, J., Smith, L., Teh, Y.W., Gal, Y.: Uncertainty estimation using a single deep deterministic neural network. In: Proceedings of the International Conference on Machine Learning (2020)
2020
Later among the works it cites.
Chen, J., Li, Y., Wu, X., Liang, Y., Jha, S.: Atom: Robustifying out-of-distribution detection using outlier mining. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (2021)
2021
Closest in time.
Huang, R., Geng, A., Li, Y.: On the importance of gradients for detecting distributional shifts in the wild. In: Proceedings of the Advances in Neural Information Processing Systems (2021)
2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks. In: Proceedings of International Conference on Learning Representations (2018)
2018
Cited alongside, same era.
Louizos, C., Welling, M., Kingma, D.P.: Learning sparse neural networks through l 0 l_{0} regularization. In: International Conference on Learning Representations (2018)
2018
Cited alongside, same era.
Malinin, A., Gales, M.: Predictive uncertainty estimation via prior networks. In: Advances in Neural Information Processing Systems. pp. 7047–7058 (2018)
2018
Cited alongside, same era.
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., Belongie, S.: The inaturalist species classification and detection dataset. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 8769–8778 (2018)
2018
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
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: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 41–50 (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Huang, R., Li, Y.: Towards scaling out-of-distribution detection for large semantic space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021)
2021
Closest in time.
Koh, P.W., Sagawa, S., Xie, S.M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R.L., Gao, I., Lee, T., et al.: Wilds: A benchmark of in-the-wild distribution shifts. In: Proceedings of the International Conference on Machine Learning. pp. 5637–5664. PMLR (2021)
2021
Closest in time.
Lin, Z., Roy, S.D., Li, Y.: Mood: Multi-level out-of-distribution detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15313–15323 (June 2021)
2021
Closest in time.
2021
Closest in time.
Sehwag, V., Chiang, M., Mittal, P.: Ssd: A unified framework for self-supervised outlier detection. In: International Conference on Learning Representations (2021)
2021
Closest in time.
Sun, Y., Guo, C., Li, Y.: React: Out-of-distribution detection with rectified activations. In: Advances in Neural Information Processing Systems (2021)
2021
Closest in time.
Wang, H., Liu, W., Bocchieri, A., Li, Y.: Can multi-label classification networks know what they don’t know? Proceedings of the Advances in Neural Information Processing Systems (2021)
2021
Closest in time.
Wong, E., Santurkar, S., Madry, A.: Leveraging sparse linear layers for debuggable deep networks. In: Proceedings of the International Conference on Machine Learning. pp. 11205–11216. PMLR (2021)
2021
Closest in time.
Yang, J., Wang, H., Feng, L., Yan, X., Zheng, H., Zhang, W., Liu, Z.: Semantically coherent out-of-distribution detection. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 8301–8309 (October 2021)
2021
Closest in time.
Zhou, K., Liu, Z., Qiao, Y., Xiang, T., Loy, C.C.: Domain generalization: A survey (2021)
2021
Closest in time.
2022
Closest in time.
Du, X., Wang, X., Gozum, G., Li, Y.: Unknown-aware object detection: Learning what you don’t know from videos in the wild. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2022)
2022
Closest in time.
Du, X., Wang, Z., Cai, M., Li, Y.: Vos: Learning what you don’t know by virtual outlier synthesis. In: Proceedings of the International Conference on Learning Representations (2022)
2022
Closest in time.
Katz-Samuels, J., Nakhleh, J., Nowak, R., Li, Y.: Training ood detectors in their natural habitats. In: Proceedings of the International Conference on Machine Learning. PMLR (2022)
2022
Closest in time.
Ming, Y., Fan, Y., Li, Y.: Poem: Out-of-distribution detection with posterior sampling. In: Proceedings of the International Conference on Machine Learning. PMLR (2022)
2022
Closest in time.
Morteza, P., Li, Y.: Provable guarantees for understanding out-of-distribution detection. Proceedings of the AAAI Conference on Artificial Intelligence (2022)
2022
Closest in time.
Sun, Y., Ming, Y., Zhu, X., Li, Y.: Out-of-distribution detection with deep nearest neighbors. In: Proceedings of the International Conference on Machine Learning (2022)
2022
Closest in time.
Wei, H., Xie, R., Cheng, H., Feng, L., An, B., Li, Y.: Mitigating neural network overconfidence with logit normalization. Proceedings of the International Conference on Machine Learning (2022)
2022
Closest in time.