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We introduce a method to provide vectorial representations of visual classification tasks which can be used to reason about the nature of those tasks and their relations.
Flat minima
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Natural gradient works efficiently in learning
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Exploiting generative models in discriminative classifiers
T. Jaakkola and D. Haussler · 1999
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Using the fisher kernel method to detect remote protein homologies
T. S. Jaakkola, M. Diekhans, and D. Haussler · 1999
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Methods of information geometry, volume 191 of translations of mathematical monographs
S.-I. Amari and H. Nagaoka · 2000
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On “natural” learning and pruning in multilayered perceptrons
T. Heskes · 2000
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M. Seeger · 2000
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String kernels, fisher kernels and finite state automata
C. Saunders, A. Vinokourov, and J. S. Shawe-taylor · 2003
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Combining generative models and fisher kernels for object recognition
A. D. Holub, M. Welling, and P. Perona · 2005
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Improving the fisher kernel for large-scale image classification
F. Perronnin, J. Sánchez, and T. Mensink · 2010
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Unbiased look at dataset bias
A. Torralba and A. A. Efros · 2011
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Learning discriminative fisher kernels
L. Van Der Maaten · 2011
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The Caltech-UCSD Birds-200-2011 Dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
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Selecting classification algorithms with active testing
R. Leite, P. Brazdil, and J. Vanschoren · 2012
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Model recommendation for action recognition
P. Matikainen, R. Sukthankar, and M. Hebert · 2012
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Image classification with the fisher vector: Theory and practice
J. Sánchez, F. Perronnin, T. Mensink, and J. Verbeek · 2013
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New perspectives on the natural gradient method
J. Martens · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Recommending learning algorithms and their associated hyperparameters
M. R. Smith, L. Mitchell, C. Giraud-Carrier, and T. Martinez · 2014
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Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al · 2017
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Visualizing the loss landscape of neural nets
H. Li, Z. Xu, G. Taylor, and T. Goldstein · 2017
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Fisher-rao metric, geometry, and complexity of neural networks
T. Liang, T. Poggio, A. Rakhlin, and J. Stokes · 2017
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Fisher gan
Y. Mroueh and T. Sercu · 2017
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https://www.kaggle.com/c/imaterialist-challenge-fashion-2018
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Predicting failures of vision systems
P. Zhang, J. Wang, A. Farhadi, M. Hebert, and D. Parikh · 2014
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Variational dropout and the local reparameterization trick
D. P. Kingma, T. Salimans, and M. Welling · 2015
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Optimizing neural networks with kronecker-factored approximate curvature
J. Martens and R. Grosse · 2015
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Model recommendation: Generating object detectors from few samples
Y.-X. Wang and M. Hebert · 2015
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H. Edwards and A. Storkey · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Deepfashion: Powering robust clothes recognition and retrieval with rich annotations
Z. Liu, P. Luo, S. Qiu, X. Wang, and X. Tang · 2016
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iMaterialist Challenge (Fashion) at FGVC5 workshop, CVPR 2018 · 2018
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Speeding up algorithm selection using average ranking and active testing by introducing runtime
S. M. Abdulrahman, P. Brazdil, J. N. van Rijn, and J. Vanschoren · 2018
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The dynamic distance between learning tasks: From Kolmogorov complexity to transfer learning via quantum physics and the information bottleneck of the weights of deep networks
A. Achille, G. Mbeng, G. Paolini, and S. Soatto · 2018
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Emergence of invariance and disentanglement in deep representations
A. Achille and S. Soatto · 2018
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Stronger generalization bounds for deep nets via a compression approach
S. Arora, R. Ge, B. Neyshabur, and Y. Zhang · 2018
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The inaturalist species classification and detection dataset
G. Van Horn, O. Mac Aodha, Y. Song, Y. Cui, C. Sun, A. Shepard, H. Adam, P. Perona, and S. Belongie · 2018
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Taskonomy: Disentangling task transfer learning
A. R. Zamir, A. Sax, W. Shen, L. Guibas, J. Malik, and S. Savarese · 2018
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Critical learning periods in deep neural networks
A. Achille, M. Rovere, and S. Soatto · 2019
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