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Automatically discovering failures in vision models under real-world settings remains an open challenge.
On a test of whether one of two random variables is stochastically larger than the other
Mann, H. B. and Whitney, D. R · 1947
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Wordnet: a lexical database for english
Miller, G. A · 1995
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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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Unbiased look at dataset bias
Torralba, A., Efros, A. A., and others · 2011
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Context models and out-of-context objects
Choi, M. J., Torralba, A., and Willsky, A. S · 2012
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Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A.-r., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., and others · 2012
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Undoing the damage of dataset bias
Khosla, A., Zhou, T., Malisiewicz, T., Efros, A. A., and Torralba, A · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Evasion Attacks against Machine Learning at Test Time
Biggio, B., Corona, I., Maiorca, D., Nelson, B., Srndic, N., Laskov, P., Giacinto, G., and Roli, F · 2013
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Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., and Vedaldi, A · 2013
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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. J., Shlens, J., and Szegedy, C · 2014
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
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End to end learning for self-driving cars
Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., and Zieba, K · 2016
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Deep Learning
Goodfellow, I., Bengio, Y., and Courville, A · 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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Adversarial examples in the physical world
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
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”why should i trust you?”: Explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Adversarial transformation networks: Learning to generate adversarial examples
Baluja, S. and Fischer, I · 2017
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Adversarial examples for evaluating reading comprehension systems
Jia, R. and Liang, P · 2017
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Gender-from-iris or gender-from-mascara?
Kuehlkamp, A., Becker, B., and Bowyer, K · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. A · 2017
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Demystifying MMD GANs
Binkowski, M., Sutherland, D. J., Arbel, M., and Gretton, A · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Buolamwini, J. and Gebru, T · 2018
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Clinically applicable deep learning for diagnosis and referral in retinal disease
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., Driessche, G. v. d., Lakshminarayanan, B., Meyer, C., Mackinder, F., Bouton, S., Ayoub, K., Chopra, R., King, D., Karthikesalingam, A., Hughes, C. O., Raine, R., Hughes, J., Sim, D. A., Egan, C., Tufail, A., Montgomery, H., Hassabis, D., Rees, G., Back, T., Khaw, P. T., Suleyman, M., Cornebise, J., Keane, P. A., and Ronneberger, O · 2018
Cited alongside, same era.
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2018
Perceptual Adversarial Robustness: Defense Against Unseen Threat Models
Laidlaw, C., Singla, S., and Feizi, S · 2020
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Beyond accuracy: Behavioral testing of NLP models with CheckList
Ribeiro, M. T., Wu, T., Guestrin, C., and Singh, S · 2020
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Sagawa, S., Koh, P. W., Hashimoto, T. B., and Liang, P · 2020
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Noise or Signal: The Role of Image Backgrounds in Object Recognition
Xiao, K., Engstrom, L., Ilyas, A., and Madry, A · 2020
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On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., Donahue, C., Doumbouya, M., Durmus, E., Ermon, S., Etchemendy, J., Ethayarajh, K., Fei-Fei, L., Finn, C., Gale, T., Gillespie, L., Goel, K., Goodman, N., Grossman, S., Guha, N., Hashimoto, T., Henderson, P., Hewitt, J., Ho, D. E., Hong, J., Hsu, K., Huang, J., Icard, T., Jain, S., Jurafsky, D., Kalluri, P., Karamcheti, S., Keeling, G., Khani, F., Khattab, O., Koh, P. W., Krass, M., Krishna, R., Kuditipudi, R., Kumar, A., Ladhak, F., Lee, M., Lee, T., Leskovec, J., Levent, I., Li, X. L., Li, X., Ma, T., Malik, A., Manning, C. D., Mirchandani, S., Mitchell, E., Munyikwa, Z., Nair, S., Narayan, A., Narayanan, D., Newman, B., Nie, A., Niebles, J. C., Nilforoshan, H., Nyarko, J., Ogut, G., Orr, L., Papadimitriou, I., Park, J. S., Piech, C., Portelance, E., Potts, C., Raghunathan, A., Reich, R., Ren, H., Rong, F., Roohani, Y., Ruiz, C., Ryan, J., Ré, C., Sadigh, D., Sagawa, S., Santhanam, K., Shih, A., Srinivasan, K., Tamkin, A., Taori, R., Thomas, A. W., Tramèr, F., Wang, R. E., Wang, W., Wu, B., Wu, J., Wu, Y., Xie, S. M., Yasunaga, M., You, J., Zaharia, M., Zhang, M., Zhang, T., Zhang, X., Zhang, Y., Zheng, L., Zhou, K., and Liang, P · 2021
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Cited alongside, same era.
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
Hendrycks, D. and Dietterich, T · 2018
Cited alongside, same era.
Semantically equivalent adversarial rules for debugging NLP models
Ribeiro, M. T., Singh, S., and Guestrin, C · 2018
Cited alongside, same era.
Constructing unrestricted adversarial examples with generative models
Song, Y., Shu, R., Kushman, N., and Ermon, S · 2018
Cited alongside, same era.
Generating adversarial examples with adversarial networks
Xiao, C., Li, B., Zhu, J.-Y., He, W., Liu, M., and Song, D · 2018
Cited alongside, same era.
Arjovsky, M., Bottou, L., Gulrajani, I., and Lopez-Paz, D · 2019
Cited alongside, same era.
Counterfactual fairness in text classification through robustness
Garg, S., Perot, V., Limtiaco, N., Taly, A., Chi, E. H., and Beutel, A · 2019
Cited alongside, same era.
Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
Gowal, S., Qin, C., Huang, P.-S., Cemgil, T., Dvijotham, K., Mann, T., and Kohli, P · 2019
Cited alongside, same era.
Later among the works it cites.
Partial success in closing the gap between human and machine vision
Geirhos, R., Narayanappa, K., Mitzkus, B., Thieringer, T., Bethge, M., Wichmann, F. A., and Brendel, W · 2021
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Scaling up vision-language pre-training for image captioning
Hu, X., Gan, Z., Wang, J., Yang, Z., Liu, Z., Lu, Y., and Wang, L · 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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Learning perturbation sets for robust machine learning
Wong, E. and Kolter, J. Z · 2021
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Meaningfully debugging model mistakes using conceptual counterfactual explanations
Abid, A., Yuksekgonul, M., and Zou, J · 2022
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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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Robust feature-level adversaries are interpretability tools
Casper, S., Nadeau, M., Hadfield-Menell, D., and Kreiman, G · 2022
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Domino: Discovering systematic errors with cross-modal embeddings
Eyuboglu, S., Varma, M., Saab, K., Delbrouck, J.-B., Lee-Messer, C., Dunnmon, J., Zou, J., and Ré, C · 2022
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Dall-e for detection: Language-driven context image synthesis for object detection
Ge, Y., Xu, J., Zhao, B. N., Itti, L., and Vineet, V · 2022
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Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2022
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Differentially private data generation needs better features
Harder, F., Asadabadi, M. J., Sutherland, D. J., and Park, M · 2022
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Medical domain knowledge in domain-agnostic generative ai
Kather, J. N., Ghaffari Laleh, N., Foersch, S., and Truhn, D · 2022
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Red teaming language models with language models
Perez, E., Huang, S., Song, F., Cai, T., Ring, R., Aslanides, J., Glaese, A., McAleese, N., and Irving, G · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 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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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., Salimans, T., Ho, J., Fleet, D. J., and Norouzi, M · 2022
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How to train your vit? data, augmentation, and regularization in vision transformers
Steiner, A., Kolesnikov, A., Zhai, X., Wightman, R., Uszkoreit, J., and Beyer, L · 2022
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