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Convolutional Neural Networks (CNNs) have become the state-of-the-art method to learn from image data.
Regularized multi–task learning
Evgeniou, T. and Pontil, M · 2004
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Deep neural networks: a new framework for modeling biological vision and brain information processing
Kriegeskorte, N · 2015
Earlier work this paper cites.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
Earlier work this paper cites.
On the performance of googlenet and alexnet applied to sketches
Ballester, P. and Araujo, R. M · 2016
Earlier work this paper cites.
Domain-adversarial training of neural networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., and Keutzer, K · 2016
Cited alongside, same era.
Texture and art with deep neural networks
Gatys, L. A., Ecker, A. S., and Bethge, M · 2017
Cited alongside, same era.
Arbitrary style transfer in real-time with adaptive instance normalization
Huang, X. and Belongie, S · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
Cited alongside, same era.
Deep convolutional neural networks for image classification: A comprehensive review
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
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Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
Brendel, W. and Bethge, M · 2019
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Dog breed identification competition, kaggle
DBI · 2019
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Dice dataset, kaggle
Dice · 2019
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Dogs versus cats competiton, kaggle
DvC · 2019
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Food101 dataset, kaggle
Food101 · 2019
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Rawat, W. and Wang, Z · 2017
Cited alongside, same era.
Cognitive psychology for deep neural networks: A shape bias case study
Ritter, S., Barrett, D. G., Santoro, A., and Botvinick, M. M · 2017
Cited alongside, same era.
Geirhos, R · 2019
Closest in time.