Fetching the paper…
Reading the bibliography…
A major problem in data augmentation is to ensure that the generated new samples cover the search space.
Lim, S., Kim, I., Kim, T., Kim, C., Kim, S., 2019 · 1905
Earlier work this paper cites.
Handwritten digit recognition with a back-propagation network, in: Advances in neural information processing systems, pp. 396–404
LeCun, Y., Boser, B.E., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W.E., Jackel, L.D., 1990 · 1990
Earlier work this paper cites.
The mnist database of handwritten digits
LeCun, Y., 1998 · 1998
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database, in: CVPR09
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., 2009 · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al., 2009 · 2009
Earlier work this paper cites.
Transforming auto-encoders, in: International Conference on Artificial Neural Networks, Springer. pp. 44–51
Hinton, G.E., Krizhevsky, A., Wang, S.D., 2011 · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y., 2011 · 2011
Earlier work this paper cites.
Multi-column deep neural networks for image classification
Cireşan, D., Meier, U., Schmidhuber, J., 2012 · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks, in: Advances in neural information processing systems, pp. 1097–1105
Krizhevsky, A., Sutskever, I., Hinton, G.E., 2012 · 2012
Earlier work this paper cites.
Better mixing via deep representations, in: International conference on machine learning, pp. 552–560
Bengio, Y., Mesnil, G., Dauphin, Y., Rifai, S., 2013 · 2013
Earlier work this paper cites.
Discriminative unsupervised feature learning with convolutional neural networks, in: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (Eds.), Advances in Neural Information Processing Systems 27. Curran Associates, Inc., pp. 766–774
Dosovitskiy, A., Springenberg, J.T., Riedmiller, M., Brox, T., 2014 · 2014
Earlier work this paper cites.
Deep directed generative autoencoders
Ozair, S., Bengio, Y., 2014 · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A., 2014 · 2014
Earlier work this paper cites.
Capturing long-tail distributions of object subcategories, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 915–922
Zhu, X., Anguelov, D., Ramanan, D., 2014 · 2014
Earlier work this paper cites.
Pareto Distribution
Arnold, B.C., 2015 · 2015
Cited alongside, same era.
Tiny imagenet visual recognition challenge
Le, Y., Yang, X., 2015 · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation, in: International Conference on Medical image computing and computer-assisted intervention, Springer. pp. 234–241
Ronneberger, O., Fischer, P., Brox, T., 2015 · 2015
Cited alongside, same era.
Going deeper with convolutions, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015 · 2015
Cited alongside, same era.
Face clustering in videos with proportion prior, in: Twenty-Fourth International Joint Conference on Artificial Intelligence
Tang, Z., Zhang, Y., Li, Z., Lu, H., 2015 · 2015
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data
Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V., 2018 · 2018
Later among the works it cites.
Matrix capsules with em routing
Hinton, G.E., Sabour, S., Frosst, N., 2018 · 2018
Later among the works it cites.
Data augmentation by pairing samples for images classification
Inoue, H., 2018 · 2018
Later among the works it cites.
Shufflenet v2: Practical guidelines for efficient cnn architecture design, in: Proceedings of the European Conference on Computer Vision (ECCV), pp. 116–131
Ma, N., Zhang, X., Zheng, H.T., Sun, J., 2018 · 2018
Later among the works it cites.
Jointly optimize data augmentation and network training: Adversarial data augmentation in human pose estimation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2226–2234
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep networks with stochastic depth, in: European conference on computer vision, Springer. pp. 646–661
Huang, G., Sun, Y., Liu, Z., Sedra, D., Weinberger, K.Q., 2016 · 2016
Cited alongside, same era.
Pillow: 3.1.0
wiredfool, Clark, A., Hugo, Murray, A., Karpinsky, A., Gohlke, C., Crowell, B., Schmidt, D., Houghton, A., Johnson, S., Mani, S., Ware, J., Caro, D., Kossouho, S., Brown, E.W., Lee, A., Korobov, M., Górny, M., Santana, E.S., Pieuchot, N., Tonnhofer, O., Brown, M., Pierre, B., Abela, J.C., Solberg, L.J., Reyes, F., Buzanov, A., Yu, Y., eliempje, Tolf, F., 2016 · 2016
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H., 2017 · 2017
Cited alongside, same era.
Densely connected convolutional networks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., 2017 · 2017
Cited alongside, same era.
Deep photo style transfer, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4990–4998
Luan, F., Paris, S., Shechtman, E., Bala, K., 2017 · 2017
Cited alongside, same era.
Dynamic routing between capsules, in: Advances in neural information processing systems, pp. 3856–3866
Sabour, S., Frosst, N., Hinton, G.E., 2017 · 2017
Cited alongside, same era.
Learning to model the tail, in: Advances in Neural Information Processing Systems, pp. 7029–7039
Wang, Y.X., Ramanan, D., Hebert, M., 2017 · 2017
Cited alongside, same era.
Peng, X., Tang, Z., Yang, F., Feris, R.S., Metaxas, D., 2018 · 2018
Later among the works it cites.
Mobilenetv2: Inverted residuals and linear bottlenecks, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4510–4520
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C., 2018 · 2018
Later among the works it cites.
Shufflenet: An extremely efficient convolutional neural network for mobile devices, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856
Zhang, X., Zhou, X., Lin, M., Sun, J., 2018 · 2018
Later among the works it cites.
Learning transferable architectures for scalable image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8697–8710
Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V., 2018 · 2018
Later among the works it cites.
Training deep models on cifar-10 and cifar-100 using autoaugment
Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V., · 2019
Closest in time.
Population based augmentation: Efficient learning of augmentation policy schedules, in: ICML
Ho, D., Liang, E., Stoica, I., Abbeel, P., Chen, X., 2019 · 2019
Closest in time.
Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning
Kuo, C.C., Chang, C.M., Liu, K.T., Lin, W.K., Chiang, H.Y., Chung, C.W., Ho, M.R., Sun, P.R., Yang, R.L., Chen, K.T., 2019 · 2019
Closest in time.
Deep model infrastructures used for training gautoaugment
Liu, K., · 2019
Closest in time.
Condensed silhouette: An optimized filtering process for cluster selection in k-means
Naghizadeh, A., Metaxas, D.N., 2020 · 2020
Closest in time.