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Learning to classify new categories based on just one or a few examples is a long-standing challenge in modern computer vision.
Caltech-256 object category dataset
G. Griffin, a. Holub, and P. Perona · 2007
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ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and F.-F. Li · 2009
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Describing Objects by their Attributes
A. Farhadi, I. Endres, D. Hoiem, and D. Forsyth · 2009
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Learning Multiple Layers of Features from Tiny Images
A. Krizhevsky · 2009
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Distance Metric Learning for Large Margin Nearest Neighbor Classification
K. Q. Weinberger and L. K. Saul · 2009
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Caltech-UCSD birds 200
P. Welinder, S. Branson, T. Mita, and C. Wah · 2010
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SUN database: Large-scale scene recognition from abbey to zoo
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba · 2010
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ImageNet Classification with Deep Convolutional Neural Networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Transfer Learning by Borrowing Examples for Multiclass Object Detection
J. J. Lim, R. Salakhutdinov, and A. Torralba · 2012
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Generative Adversarial Nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan and A. Zisserman · 2014
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Predicting deep zero-shot convolutional neural networks using textual descriptions
J. L. Ba, K. Swersky, S. Fidler, and R. Salakhutdinov · 2015
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Transductive Multi-View Zero-Shot Learning
Y. Fu, T. M. Hospedales, T. Xiang, and S. Gong · 2015
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Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Human-level concept learning through probabilistic program induction
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum · 2015
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Articulated pose estimation with tiny synthetic videos
D. Park and D. Ramanan · 2015
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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
A. Radford, L. Metz, and S. Chintala · 2015
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Metric Learning with Adaptive Density Discrimination
O. Rippel, M. Paluri, P. Dollar, and L. Bourdev · 2015
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FaceNet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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Render for CNN Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views.pdf
H. Su, C. R. Qi, Y. Li, and L. J. Guibas · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
Semi-supervised Vocabulary-informed Learning
Y. Fu and L. Sigal · 2016
Cited alongside, same era.
Generative Visual Manipulation on the Natural Image Manifold
E. S. Jun-Yan Zhu, Philipp Krahenbuhl and A. Efros · 2016
Cited alongside, same era.
Learning without Forgetting
Z. Li and D. Hoiem · 2016
Cited alongside, same era.
Low-shot Visual Recognition by Shrinking and Hallucinating Features
B. Hariharan and R. Girshick · 2017
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Densely Connected Convolutional Networks
G. Huang, Z. Liu, L. v. d. Maaten, and K. Q. Weinberger · 2017
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Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
Z. Li, F. Zhou, F. Chen, and H. Li · 2017
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Focal Loss for Dense Object Detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
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Generative Adversarial Residual Pairwise Networks for One Shot Learning
A. Mehrotra and A. Dukkipati · 2017
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Least Squares Generative Adversarial Networks
X. Mao, Q. Li, H. Xie, R. Y. K. Lau, Z. Wang, and S. P. Smolley · 2016
Cited alongside, same era.
Matching Networks for One Shot Learning
O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra · 2016
Cited alongside, same era.
Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs
Y.-X. Wang and M. Hebert · 2016
Cited alongside, same era.
Learning to Learn: Model Regression Networks for Easy Small Sample Learning
Y.-X. Wang and M. Hebert · 2016
Cited alongside, same era.
Data Augmentation Generative Adversarial Networks
A. Antoniou, A. Storkey, and H. Edwards · 2017
Cited alongside, same era.
Generating Visual Representations for Zero-Shot Classification
M. Bucher, S. Herbin, and F. Jurie · 2017
Cited alongside, same era.
T. Munkhdalai and H. Yu · 2017
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Optimization As a Model for Few-Shot Learning
S. Ravi and H. Larochelle · 2017
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Prototypical Networks for Few-shot Learning
J. Snell, K. Swersky, and R. S. Zemel · 2017
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Learning to Compare: Relation Network for Few-Shot Learning
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. S. Torr, and T. M. Hospedales · 2017
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Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images
A. Yu and K. Grauman · 2017
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Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
J. Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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Learning Transferable Architectures for Scalable Image Recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2017
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Semantic Feature Augmentation in Few-shot Learning
Z. Chen, Y. Fu, Y. Zhang, Y.-G. Jiang, X. Xue, and L. Sigal · 2018
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Few-Shot Learning with Metric-Agnostic Conditional Embeddings
N. Hilliard, L. Phillips, S. Howland, A. Yankov, C. D. Corley, and N. O. Hodas · 2018
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Few-shot autoregressive density estimation: towards learning to learn distributions
S. Reed, Y. Chen, T. Paine, A. van den Oord, S. M. A. Eslami, D. Rezende, O. Vinyals, and N. de Freitas · 2018
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Low-Shot Learning from Imaginary Data
Y.-X. Wang, R. Girshick, M. Hebert, and B. Hariharan · 2018
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Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly
Y. Xian, C. H. Lampert, B. Schiele, and Z. Akata · 2018
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Deep Meta-Learning: Learning to Learn in the Concept Space
F. Zhou, B. Wu, and Z. Li · 2018
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