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Among the three main components (data, labels, and models) of any supervised learning system, data and models have been the main subjects of active research.
Introduction to wordnet: An on-line lexical database
Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.J.: · 1990
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Verbs semantics and lexical selection
Wu, Z., Palmer, M.: · 1994
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Model compression
Buciluǎ, C., Caruana, R., Niculescu-Mizil, A.: · 2006
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Exploiting known taxonomies in learning overlapping concepts
Cai, L., Hofmann, T.: · 2007
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2014
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Training deep neural networks on noisy labels with bootstrapping
Reed, S., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., Rabinovich, A.: · 2014
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Do deep nets really need to be deep?
Ba, J., Caruana, R.: · 2014
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Learning small-size dnn with output-distribution-based criteria
Li, J., Zhao, R., Huang, J.T., Gong, Y.: · 2014
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Fitnets: Hints for thin deep nets
Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: · 2014
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
Cited alongside, same era.
Distributional smoothing by virtual adversarial examples
Miyato, T., Maeda, S.i., Koyama, M., Nakae, K., Ishii, S.: · 2015
Cited alongside, same era.
Ml-mg: Multi-label learning with missing labels using a mixed graph
Wu, B., Lyu, S., Ghanem, B.: · 2015
Cited alongside, same era.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., Dean, J.: · 2015
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: · 2016
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The effectiveness of data augmentation in image classification using deep learning
Wang, J., Perez, L.: · 2017
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Learning from simulated and unsupervised images through adversarial training
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: · 2017
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Regularizing neural networks by penalizing confident output distributions
Pereyra, G., Tucker, G., Chorowski, J., Kaiser, Ł., Hinton, G.: · 2017
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Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., et al.: · 2015
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., Clune, J.: · 2015
Cited alongside, same era.
Understanding data augmentation for classification: when to warp?
Wong, S.C., Gatt, A., Stamatescu, V., McDonnell, M.D.: · 2016
Cited alongside, same era.
Improved relation classification by deep recurrent neural networks with data augmentation
Xu, Y., Jia, R., Mou, L., Li, G., Chen, Y., Lu, Y., Jin, Z.: · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Cited alongside, same era.
Disturblabel: Regularizing cnn on the loss layer
Xie, L., Wang, J., Wei, Z., Wang, M., Tian, Q.: · 2016
Cited alongside, same era.
Optimization of robust loss functions for weakly-labeled image taxonomies: An imagenet case study
McAuley, J.J., Ramisa, A., Caetano, T.S.:
Cited in the paper.
Li, Y., Yang, J., Song, Y., Cao, L., Luo, J., Li, J.: · 2017
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Hierarchical loss for classification
Wu, C., Tygert, M., LeCun, Y.: · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: · 2017
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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
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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: · 2017
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Yolo9000: Better, faster, stronger
Redmon, J., Farhadi, A.: · 2017
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