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Out-of-distribution (OOD) detection is a critical requirement for the deployment of deep neural networks.
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One
Grathwohl, W., Wang, K.-C., Jacobsen, J.-H., Duvenaud, D., Norouzi, M., and Swersky, K · 1912
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Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E · 2002
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A tutorial on energy-based learning
LeCun, Y., Chopra, S., Hadsell, R., and Huang, F. J · 2006
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Training restricted boltzmann machines using approximations to the likelihood gradient
Tieleman, T · 2008
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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 · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
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MCMC using Hamiltonian dynamics
Neal, R. M · 2010
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VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models
Xiao, Z., Kreis, K., Kautz, J., and Vahdat, 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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Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W · 2011
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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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Towards open world recognition
Bendale, A. and Boult, T. E · 2015
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LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Zhang, Y., Song, S., Seff, A., and Xiao, J · 2015
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Concrete Problems in AI Safety
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., and Mané, D · 2016
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Towards open set deep networks
Bendale, A. and Boult, T. E · 2016
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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 theory of generative convnet
Xie, J., Lu, Y., Zhu, S., and Wu, Y. N · 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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Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 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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Predictive uncertainty estimation via prior networks
Malinin, A. and Gales, M · 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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Cooperative learning of energy-based model and latent variable model via MCMC teaching
Xie, J., Lu, Y., Gao, R., and Wu, Y. N · 2018
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Implicit generation and modeling with energy-based models
Du, Y. and Mordatch, I · 2019
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Deep Anomaly Detection with Outlier Exposure
Hendrycks, D., Mazeika, M., and Dietterich, T · 2019
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Unsupervised Learning of Compositional Energy Concepts
Du, Y., Li, S., Sharma, Y., Tenenbaum, J. B., and Mordatch, I · 2021
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On Out-of-distribution Detection with Energy-based Models
Elflein, S., Charpentier, B., Zügner, D., and Günnemann, S · 2021
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On the importance of gradients for detecting distributional shifts in the wild
Huang, R., Geng, A., and Li, Y · 2021
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Learning Transferable Visual Models From Natural Language Supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I · 2021
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SSD: A unified framework for self-supervised outlier detection
Sehwag, V., Chiang, M., and Mittal, P · 2021
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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., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Cited alongside, same era.
Pytorch image models
Wightman, R · 2019
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Posterior network: Uncertainty estimation without ood samples via density-based pseudo-counts
Charpentier, B., Zügner, D., and Günnemann, S · 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., et al · 2020
Cited alongside, same era.
Compositional Visual Generation with Energy Based Models
Du, Y., Li, S., and Mordatch, I · 2020
Cited alongside, same era.
Computer vision for autonomous vehicles: Problems, datasets and state of the art
Janai, J., Güney, F., Behl, A., and Geiger, A · 2020
Cited alongside, same era.
How to train your energy-based models
Song, Y. and Kingma, D. P · 2021
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Learning energy-based model with variational auto-encoder as amortized sampler
Xie, J., Zheng, Z., and Li, P · 2021
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Cooperative training of fast thinking initializer and slow thinking solver for conditional learning
Xie, J., Zheng, Z., Fang, X., Zhu, S., and Wu, Y. N · 2021
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Augmenting physical models with deep networks for complex dynamics forecasting
Yin, Y., Le Guen, V., Dona, J., Ayed, I., de Bézenac, E., Thome, N., and Gallinari, P · 2021
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Flamingo: a Visual Language Model for Few-Shot Learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., Ring, R., Rutherford, E., Cabi, S., Han, T., Gong, Z., Samangooei, S., Monteiro, M., Menick, J., Borgeaud, S., Brock, A., Nematzadeh, A., Sharifzadeh, S., Binkowski, M., Barreira, R., Vinyals, O., Zisserman, A., and Simonyan, K · 2022
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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 · 2022
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Complementing brightness constancy with deep networks for optical flow prediction
Le Guen, V., Rambour, C., and Thome, N · 2022
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MCMC should mix: Learning energy-based model with neural transport latent space MCMC
Nijkamp, E., Gao, R., Sountsov, P., Vasudevan, S., Pang, B., Zhu, S., and Wu, Y. N · 2022
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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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Vim: Out-of-distribution with virtual-logit matching
Wang, H., Li, Z., Feng, L., and Zhang, W · 2022
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A tale of two flows: Cooperative learning of langevin flow and normalizing flow toward energy-based model
Xie, J., Zhu, Y., Li, J., and Li, P · 2022
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OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Yang, J., Wang, P., Zou, D., Zhou, Z., Ding, K., Peng, W., Wang, H., Chen, G., Li, B., Sun, Y., Du, X., Zhou, K., Zhang, W., Hendrycks, D., Li, Y., and Liu, Z · 2022
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