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Fine-Grained Visual Classification (FGVC) datasets contain small sample sizes, along with significant intra-class variation and inter-class similarity.
On information and sufficiency
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: · 1958
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A simple weight decay can improve generalization
Krogh, A., Hertz, J.A.: · 1991
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The theory of probability
Jeffreys, H.: · 1998
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Learning a similarity metric discriminatively, with application to face verification
Chopra, S., Hadsell, R., LeCun, Y.: · 2005
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Automated flower classification over a large number of classes
Nilsback, M.E., Zisserman, A.: · 2008
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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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Combining randomization and discrimination for fine-grained image categorization
Yao, B., Khosla, A., Fei-Fei, L.: · 2011
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Relative attributes
Parikh, D., Grauman, K.: · 2011
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The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: · 2011
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Novel dataset for fine-grained image categorization: Stanford dogs
Khosla, A., Jayadevaprakash, N., Yao, B., Li, F.F.: · 2011
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: · 2011
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A codebook-free and annotation-free approach for fine-grained image categorization
Yao, B., Bradski, G., Fei-Fei, L.: · 2012
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Pose pooling kernels for sub-category recognition
Zhang, N., Farrell, R., Darrell, T.: · 2012
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3d object representations for fine-grained categorization
Krause, J., Stark, M., Deng, J., Fei-Fei, L.: · 2013
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Fine-grained visual classification of aircraft
Maji, S., Rahtu, E., Kannala, J., Blaschko, M., Vedaldi, A.: · 2013
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Energy statistics: A class of statistics based on distances
Székely, G.J., Rizzo, M.L.: · 2013
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Efficient object detection and segmentation for fine-grained recognition
Angelova, A., Zhu, S.: · 2013
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Part-based r-cnns for fine-grained category detection
Zhang, N., Donahue, J., Girshick, R., Darrell, T.: · 2014
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Bird species categorization using pose normalized deep convolutional nets
Branson, S., Van Horn, G., Belongie, S., Perona, P.: · 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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The cifar-10 dataset otkrist (2014)
Krizhevsky, A., Nair, V., Hinton, G.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
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Compact bilinear pooling
Gao, Y., Beijbom, O., Zhang, N., Darrell, T.: · 2016
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Mining discriminative triplets of patches for fine-grained classification
Wang, Y., Choi, J., Morariu, V., Davis, L.S.: · 2016
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Boosted convolutional neural networks
Moghimi, M., Saberian, M., Yang, J., Li, L.J., Vasconcelos, N., Belongie, S.: · 2016
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Deep relative attributes
Souri, Y., Noury, E., Adeli, E.: · 2016
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Deep learning the city: Quantifying urban perception at a global scale
Dubey, A., Naik, N., Parikh, D., Raskar, R., Hidalgo, C.A.: · 2016
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End-to-end localization and ranking for relative attributes
Singh, K.K., Lee, Y.J.: · 2016
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CNN features off-the-shelf: An astounding baseline for recognition
Sharif Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: · 2014
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Bilinear cnn models for fine-grained visual recognition
Lin, T.Y., RoyChowdhury, A., Maji, S.: · 2015
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Fine-grained recognition without part annotations
Krause, J., Jin, H., Yang, J., Fei-Fei, L.: · 2015
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Fine-grained pose prediction, normalization, and recognition
Zhang, N., Shelhamer, E., Gao, Y., Darrell, T.: · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
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Learning from massive noisy labeled data for image classification
Xiao, T., Xia, T., Yang, Y., Huang, C., Wang, X.: · 2015
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Adding gradient noise improves learning for very deep networks
Neelakantan, A., Vilnis, L., Le, Q.V., Sutskever, I., Kaiser, L., Kurach, K., Martens, J.: · 2015
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: · 2016
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Picking deep filter responses for fine-grained image recognition
Zhang, X., Xiong, H., Zhou, W., Lin, W., Tian, Q.: · 2016
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Hierarchical joint cnn-based models for fine-grained cars recognition
Liu, M., Yu, C., Ling, H., Lei, J.: · 2016
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Selvaraju, R.R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., Batra, D.: · 2016
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Improved bilinear pooling with cnns
Lin, T.Y., Maji, S.: · 2017
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Kernel pooling for convolutional neural networks
Cui, Y., Zhou, F., Wang, J., Liu, X., Lin, Y., Belongie, S.: · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: · 2017
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Modeling image virality with pairwise spatial transformer networks
Dubey, A., Agarwal, S.: · 2017
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Tensors and Dynamic neural networks in Python with strong GPU acceleration
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Generalized orderless pooling performs implicit salient matching
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Low-rank bilinear pooling for fine-grained classification
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Spatial transformer networks
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