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We present You Only Cut Once (YOCO) for performing data augmentations.
Texture synthesis by non-parametric sampling
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Improved baselines with momentum contrastive learning
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Video google: A text retrieval approach to object matching in videos
Sivic, J. and Zisserman, A · 2003
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Visual categorization with bags of keypoints
Csurka, G., Dance, C., Fan, L., Willamowski, J., and Bray, C · 2004
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Optimal spatial adaptation for patch-based image denoising
Kervrann, C. and Boulanger, J · 2006
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Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories
Lazebnik, S., Schmid, C., and Ponce, J · 2006
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Automated flower classification over a large number of classes
Nilsback, M.-E. and Zisserman, A · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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The pascal visual object classes (voc) challenge
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Semantic image segmentation with deep convolutional nets and fully connected crfs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., and Darrell, T · 2015
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Posterior calibration and exploratory analysis for natural language processing models
Nguyen, K. and O’Connor, B · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J · 2015
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Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al · 2015
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Deep networks with stochastic depth
Huang, G., Sun, Y., Liu, Z., Sedra, D., and Weinberger, K. Q · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Wide residual networks
Zagoruyko, S. and Komodakis, N · 2016
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Ntire 2017 challenge on single image super-resolution: Dataset and study
Agustsson, E. and Timofte, R · 2017
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Xception: Deep learning with depthwise separable convolutions
Chollet, F · 2017
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Improved regularization of convolutional neural networks with cutout
DeVries, T. and Taylor, G. W · 2017
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Cut, paste and learn: Surprisingly easy synthesis for instance detection
Dwibedi, D., Misra, I., and Hebert, M · 2017
Toward real-world single image super-resolution: A new benchmark and a new model
Cai, J., Zeng, H., Yong, H., Cao, Z., and Zhang, L · 2019
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Improving robustness without sacrificing accuracy with patch gaussian augmentation
Lopes, R. G., Yin, D., Poole, B., Gilmer, J., and Cubuk, E. D · 2019
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Pytorch image models
Wightman, R · 2019
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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
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Randaugment: Practical automated data augmentation with a reduced search space
Cubuk, E. D., Zoph, B., Shlens, J., and Le, Q. V · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
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Cited alongside, same era.
Synthesizing training data for object detection in indoor scenes
Georgakis, G., Mousavian, A., Berg, A. C., and Kosecka, J · 2017
Cited alongside, same era.
Mask r-cnn
He, K., Gkioxari, G., Dollár, P., and Girshick, R · 2017
Cited alongside, same era.
Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
Cited alongside, same era.
Deep feature interpolation for image content changes
Upchurch, P., Gardner, J., Pleiss, G., Pless, R., Snavely, N., Bala, K., and Weinberger, K · 2017
Cited alongside, same era.
Aggregated residual transformations for deep neural networks
Xie, S., Girshick, R., Dollár, P., Tu, Z., and He, K · 2017
Cited alongside, same era.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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Torchattacks: A pytorch repository for adversarial attacks
Kim, H · 2020
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Contrastive learning for unpaired image-to-image translation
Park, T., Efros, A. A., Zhang, R., and Zhu, J.-Y · 2020
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Rethinking data augmentation for image super-resolution: A comprehensive analysis and a new strategy
Yoo, J., Ahn, N., and Sohn, K.-A · 2020
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Random erasing data augmentation
Zhong, Z., Zheng, L., Kang, G., Li, S., and Yang, Y · 2020
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Jigsaw clustering for unsupervised visual representation learning
Chen, P., Liu, S., and Jia, J · 2021
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Exploring simple siamese representation learning
Chen, X. and He, K · 2021
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Co-mixup: Saliency guided joint mixup with supermodular diversity
Kim, J.-H., Choo, W., Jeong, H., and Song, H. O · 2021
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Local patch autoaugment with multi-agent collaboration
Lin, S., Yu, T., Feng, R., Li, X., Jin, X., and Chen, Z · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B · 2021
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Qin, Y., Zhang, C., Chen, T., Lakshminarayanan, B., Beutel, A., and Wang, X · 2021
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Regularizing deep networks with semantic data augmentation
Wang, Y., Huang, G., Song, S., Pan, X., Xia, Y., and Wu, C · 2021
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Multi-stage progressive image restoration
Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., Yang, M.-H., and Shao, L · 2021
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The effects of regularization and data augmentation are class dependent
Balestriero, R., Bottou, L., and LeCun, Y · 2022
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