Fetching the paper…
Reading the bibliography…
Despite apparent human-level performances of deep neural networks (DNN), they behave fundamentally differently from humans.
Random erasing data augmentation
Zhong, Z., Zheng, L., Kang, G., Li, S., and Yang, Y · 2003
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.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
Deep networks with stochastic depth
Huang, G., Sun, Y., Liu, Z., Sedra, D., and Weinberger, K · 2016
Earlier work this paper cites.
Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
Earlier work this paper cites.
Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
Cited alongside, same era.
Gastaldi, X · 2017
Cited alongside, same era.
Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
Cited alongside, same era.
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
Deep pyramidal residual networks
Han, D., Kim, J., and Kim, J · 2017
Cited alongside, same era.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2017
Later among the works it cites.
Autoaugment: Learning augmentation policies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2018
Later among the works it cites.
Dropblock: A regularization method for convolutional networks
Ghiasi, G., Lin, T.-Y., and Le, Q. V · 2018
Later among the works it cites.
Gather-excite: Exploiting feature context in convolutional neural networks
Hu, J., Shen, L., Albanie, S., Sun, G., and Vedaldi, A · 2018
Later among the works it cites.
Kannan, H., Kurakin, A., and Goodfellow, I · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hendrycks, D. and Gimpel, K · 2017
Cited alongside, same era.
Squeeze-and-excitation networks
Hu, J., Shen, L., and Sun, G · 2017
Cited alongside, same era.
Densely connected convolutional networks
Huang, G., Liu, Z., and Weinberger, K. Q · 2017
Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?
Kendall, A. and Gal, Y · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Cited alongside, same era.
Attacking the madry defense model with l _ 1 l\_1 -based adversarial examples
Sharma, Y. and Chen, P.-Y · 2017
Cited alongside, same era.
NSML: Meet the mlaas platform with a real-world case study
Kim, H., Kim, M., Seo, D., Kim, J., Park, H., Park, S., Jo, H., Kim, K., Yang, Y., Kim, Y., et al · 2018
Later among the works it cites.
Training confidence-calibrated classifiers for detecting out-of-distribution samples
Lee, K., Lee, H., Lee, K., and Shin, J · 2018
Later among the works it cites.
Enhancing the reliability of out-of-distribution image detection in neural networks
Liang, S., Li, Y., and Srikant, R · 2018
Later among the works it cites.
Feature denoising for improving adversarial robustness
Xie, C., Wu, Y., van der Maaten, L., Yuille, A., and He, K · 2018
Later among the works it cites.
Shakedrop regularization for deep residual learning
Yamada, Y., Iwamura, M., Akiba, T., and Kise, K · 2018
Later among the works it cites.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
Later among the works it cites.
Robustness may be at odds with accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A · 2019
Later among the works it cites.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., and Yoo, Y · 2019
Later among the works it cites.