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With the advent of large labelled datasets and high-capacity models, the performance of machine vision systems has been improving rapidly.
Multitask learning
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Y. LeCun, C. Cortes, and C. J. Burges · 1998
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Lifelong learning algorithms
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The Developing Visual Brain
J. Atkinson · 2002
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An experimental study on pedestrian classification
S. Munder and D. M. Gavrila · 2006
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Caltech-256 object category dataset
G. Griffin, A. Holub, and P. Perona · 2007
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Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
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Learning to detect unseen object classes by between-class attribute transfer
C. H. Lampert, H. Nickisch, and S. Harmeling · 2009
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Never-ending learning
T. Mitchell · 2010
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Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
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How do humans sketch objects?
M. Eitz, J. Hays, and M. Alexa · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel · 2012
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Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2013
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Lifelong machine learning systems: Beyond learning algorithms
D. L. Silver, Q. Yang, and L. Li · 2013
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Regularization of neural networks using dropconnect
L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, and R. Fergus · 2013
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Robust visual tracking via structured multi-task sparse learning
T. Zhang, B. Ghanem, S. Liu, and N. Ahuja · 2013
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Return of the devil in the details: Delving deep into convolutional nets
K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Deep convolutional filter banks for texture recognition and segmentation
M. Cimpoi, S. Maji, and A. Vedaldi · 2014
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Synthetic data and artificial neural networks for natural scene text recognition
M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 2014
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Fitnets: Hints for thin deep nets
A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio · 2014
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Imagenet large scale visual recognition challenge, 2014
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2014
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Deepface: Closing the gap to human-level performance in face verification
Y. Taigman, M. Yang, M. A. Ranzato, and L. Wolf · 2014
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Visualizing and understanding convolutional networks
Knowledge transfer in deep block-modular neural networks
A. V. Terekhov, G. Montone, and J. K. O’Regan · 2015
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Simultaneous deep transfer across domains and tasks
E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko · 2015
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Designing deep networks for surface normal estimation
X. Wang, D. Fouhey, and A. Gupta · 2015
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Layer normalization
J. L. Ba, J. R. Kiros, and G. E. Hinton · 2016
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Integrated perception with recurrent multi-task neural networks
H. Bilen and A. Vedaldi · 2016
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Instance-aware semantic segmentation via multi-task network cascades
J. Dai, K. He, and J. Sun · 2016
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M. D. Zeiler and R. Fergus · 2014
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Facial landmark detection by deep multi-task learning
Z. Zhang, P. Luo, C. C. Loy, and X. Tang · 2014
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Net2net: Accelerating learning via knowledge transfer
T. Chen, I. Goodfellow, and J. Shlens · 2015
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Planktonset 1.0: Plankton imagery data collected from f.g. walton smith in straits of florida from 2014-06-03 to 2014-06-06 and used in the 2015 national data science bowl
R. K. Cowen, S. Sponaugle, K. Robinson, and J. Luo · 2015
Cited alongside, same era.
Unsupervised domain adaptation by backpropagation
Y. Ganin and V. Lempitsky · 2015
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
Cited alongside, same era.
Instance-aware semantic segmentation via multi-task network cascades
J. Dai, K. He, and J. Sun · 2016
Later among the works it cites.
Synthetic data for text localisation in natural images
A. Gupta, A. Vedaldi, and A. Zisserman · 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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Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Faces in places: Compound query retrieval
Y. Hong, R. Arandjelović, and A. Zisserman · 2016
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Reading text in the wild with convolutional neural networks
M. Jaderberg, K. Simonyan, A. Vedaldi, and A. Zisserman · 2016
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Spinenet: Automatically pinpointing classification evidence in spinal mris
A. Jamaludin, T. Kadir, and A. Zisserman · 2016
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I. Kokkinos · 2016
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Generalizing pooling functions in convolutional neural networks: Mixed, gated, and tree
C.-Y. Lee, P. W. Gallagher, and Z. Tu · 2016
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Learning without forgetting
Z. Li and D. Hoiem · 2016
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Ssd: Single shot multibox detector
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg · 2016
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Cross-stitch networks for multi-task learning
I. Misra, A. Shrivastava, A. Gupta, and M. Hebert · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, and J. Shlens · 2016
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Instance normalization: The missing ingredient for fast stylization
D. Ulyanov, A. Vedaldi, and V. S. Lempitsky · 2016
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