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Detecting out-of-distribution (OOD) samples is essential when deploying machine learning models in open-world scenarios.
Scaling out-of-distribution detection for real-world settings
Hendrycks, D., Basart, S., Mazeika, M., Zou, A., Kwon, J., Mostajabi, M., Steinhardt, J., and Song, D · 1911
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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., Hinton, G., et al · 2009
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Sun database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., and Torralba, A · 2010
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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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The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
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Cats and dogs
Parkhi, O. M., Vedaldi, A., Zisserman, A., and Jawahar, C · 2012
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3d object representations for fine-grained categorization
Krause, J., Stark, M., Deng, J., and Fei-Fei, L · 2013
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Food-101 – mining discriminative components with random forests
Bossard, L., Guillaumin, M., and Van Gool, L · 2014
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Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., and Vedaldi, A · 2014
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Accelerating t-sne using tree-based algorithms
Van Der Maaten, L · 2014
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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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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D. and Gimpel, K · 2017
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Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., and Srikant, R · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., and Torralba, A · 2017
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Improving reconstruction autoencoder out-of-distribution detection with mahalanobis distance
Denouden, T., Salay, R., Czarnecki, K., Abdelzad, V., Phan, B., and Vernekar, S · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Lee, K., Lee, K., Lee, H., and Shin, J · 2018
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Sharma, P., Ding, N., Goodman, S., and Soricut, R · 2018
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The inaturalist species classification and detection dataset
Van Horn, G., Mac Aodha, O., Song, Y., Cui, Y., Sun, C., Shepard, A., Adam, H., Perona, P., and Belongie, S · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
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Likelihood ratios for out-of-distribution detection
Ren, J., Liu, P. J., Fertig, E., Snoek, J., Poplin, R., Depristo, M., Dillon, J., and Lakshminarayanan, B · 2019
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Learning robust global representations by penalizing local predictive power
Wang, H., Ge, S., Lipton, Z., and Xing, E. P · 2019
Cited alongside, same era.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Dice: Leveraging sparsification for out-of-distribution detection
Sun, Y. and Li, Y · 2022
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Out-of-distribution detection with deep nearest neighbors
Sun, Y., Ming, Y., Zhu, X., and Li, Y · 2022
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Open-set recognition: A good closed-set classifier is all you need?
Vaze, S., Han, K., Vedaldi, A., and Zisserman, A · 2022
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Watermarking for out-of-distribution detection
Wang, Q., Liu, F., Zhang, Y., Zhang, J., Gong, C., Liu, T., and Han, B · 2022
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Groupvit: Semantic segmentation emerges from text supervision
Xu, J., De Mello, S., Liu, S., Byeon, W., Breuel, T., Kautz, J., and Wang, X · 2022
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Energy-based out-of-distribution detection
Liu, W., Wang, X., Owens, J., and Li, Y · 2020
Cited alongside, same era.
Contrastive multiview coding
Tian, Y., Krishnan, D., and Isola, P · 2020
Cited alongside, same era.
Likelihood regret: An out-of-distribution detection score for variational auto-encoder
Xiao, Z., Yan, Q., and Amit, Y · 2020
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
Cited alongside, same era.
Exploring the limits of out-of-distribution detection
Fort, S., Ren, J., and Lakshminarayanan, B · 2021
Cited alongside, same era.
Mos: Towards scaling out-of-distribution detection for large semantic space
Huang, R. and Li, Y · 2021
Cited alongside, same era.
On the importance of gradients for detecting distributional shifts in the wild
Huang, R., Geng, A., and Li, Y · 2021
Cited alongside, same era.
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Optimizing two-way partial auc with an end-to-end framework
Yang, Z., Xu, Q., Bao, S., He, Y., Cao, X., and Huang, Q · 2022
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Rethinking reconstruction autoencoder-based out-of-distribution detection
Zhou, Y · 2022
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Claude 2
Anthropic · 2023
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In or out? fixing imagenet out-of-distribution detection evaluation
Bitterwolf, J., Mueller, M., and Hein, M · 2023
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Unsupervised out-of-distribution detection with diffusion inpainting
Liu, Z., Zhou, J. P., Wang, Y., and Weinberger, K. Q · 2023
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Enhancing clip with gpt-4: Harnessing visual descriptions as prompts
Maniparambil, M., Vorster, C., Molloy, D., Murphy, N., McGuinness, K., and O’Connor, N. E · 2023
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Visual classification via description from large language models
Menon, S. and Vondrick, C · 2023
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OpenAI · 2023
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Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al · 2023
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Clipn for zero-shot ood detection: Teaching clip to say no
Wang, H., Li, Y., Yao, H., and Li, X · 2023
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Openood v1.5: Enhanced benchmark for out-of-distribution detection
Zhang, J., Yang, J., Wang, P., Wang, H., Lin, Y., Zhang, H., Sun, Y., Du, X., Zhou, K., Zhang, W., Li, Y., Liu, Z., Chen, Y., and Li, H · 2023
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Unleashing mask: Explore the intrinsic out-of-distribution detection capability
Zhu, J., Li, H., Yao, J., Liu, T., Xu, J., and Han, B · 2023
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The claude 3 model family: Opus, sonnet, haiku
Anthropic, A · 2024
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Jiang, A. Q., Sablayrolles, A., Roux, A., Mensch, A., Savary, B., Bamford, C., Chaplot, D. S., Casas, D. d. l., Hanna, E. B., Bressand, F., et al · 2024
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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Reid, M., Savinov, N., Teplyashin, D., Lepikhin, D., Lillicrap, T., Alayrac, J.-b., Soricut, R., Lazaridou, A., Firat, O., Schrittwieser, J., et al · 2024
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