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The ability to quickly recognize and learn new visual concepts from limited samples enables humans to swiftly adapt to new environments.
Signature verification using a siamese time delay neural network
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Learning To Learn: Introduction
Thrun, S.: · 1996
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A perspective view and survey of meta-learning
JVilalta, R., Drissi, Y.: · 2002
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A bayesian approach to unsupervised one-shot learning of object categories
Fei-Fei, L., Fergus, R., Perona, P.: · 2003
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Cross-generalization: learning novel classes from a single example by feature replacement
Bart, E., Ullman, S.: · 2005
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Pattern recognition from one example by chopping
Fleuret, F., Blanchard, G.: · 2005
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Robust boosting for learning from few examples
Wolf, L., Martin, I.: · 2005
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Object classification from a single example utilizing class relevance metrics
Fink, M.: · 2005
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Incremental learning of object detectors using a visual shape alphabet
Opelt, A., Pinz, A., Zisserman, A.: · 2006
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One-shot learning of object categories
Fei-Fei, L., Fergus, R., Perona, P.: · 2006
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Personalized handwriting recognition via biased reg- ularization
Kienzle, W., Chellapilla, K.: · 2006
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reducing the dimensionality of data with neural networks
Hinton, G.E., Salakhutdinov, R.R.: · 2006
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Uncovering shared structures in multiclass classification
Amit, Y., Fink, M., S., N., U.: · 2007
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sharing visual features for multiclass and multiview object detection
Torralba, A., Murphy, K., Freeman, W.: · 2007
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Caltech-256 object category dataset
Griffin, G., Holub, A., Perona, P.: · 2007
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Transfer learning for image classification with sparse prototype representations
Quattoni, A., Collins, M., Darrell, T.: · 2008
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The more you know, the less you learn: from knowledge transfer to one-shot learning of object categories
Tommasi, T., Caputo, B.: · 2009
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Describing objects by their attributes
Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: · 2009
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The one-shot similarity kernel
Wolf, L., Hassner, T., Taigman, Y.: · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A.: · 2009
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What helps where – and why? semantic relatedness for knowledge transfer
Rohrbach, M., Stark, M., Szarvas, G., Gurevych, I., Schiele, B.: · 2010
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Using the forest to see the trees: Exploiting context for visual object detection and localization
Torralba, A., Murphy, K.P., Freeman, W.T.: · 2010
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Transfer learning by borrowing examples for multiclass object detection
Lim, J., Salakhutdinov, R., Torralba, A.: · 2011
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Meta-learning in computational intelligence
Jankowski, Norbert, Duch, Wodzislaw, Grabczewski, Krzyszto: · 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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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Distributed representations of words and phrases and their compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: · 2013
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One-shot learning by inverting a compositional causal process
Lake, B.M., Salakhutdinov, R.: · 2013
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Attribute-based classification for zero-shot visual object categorization
Lampert, C.H., Nickisch, H., Harmeling, S.: · 2013
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Transfer learning in a transductive setting
Rohrbach, M., Ebert, S., Schiele, B.: · 2013
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Auto-encoding variational bayes
Kingma, D., Welling, M.: · 2014
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Return of the devil in the details: Delving deep into convolutional nets
Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: · 2014
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Visualizing and understanding convolutional networks
Zeiler, M.D., Fergus, R.: · 2014
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Generative adversarial nets
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., DavidWarde-Farley, Ozair, S., Courville, A., Bengio, Y.: · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: · 2014
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Understanding Deep Image Representations by Inverting Them
Mahendran, A., Vedaldi, A.: · 2014
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Transductive multi-view zero-shot learning
Fu, Y., Hospedales, T.M., Xiang, T., Gong, S.: · 2015
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Dataset curation through renders and ontology matching
Movshovitz-Attias, Y.: · 2015
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Wang, J., Wei, Z., Zhang, T., Zeng, W.: · 2016
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A simple neural attentive meta-learner
Mishra, N., Rohaninejad, M., Chen, X., Abbeel, P.: · 2016
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Aga: Attribute guided augmentation
Dixit, M., Kwitt, R., Niethammer, M., Vasconcelos, N.: · 2017
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Prototypical networks for few-shot learning
Snell, J., Swersky, K., Zemeln, R.S.: · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., Levine, S.: · 2017
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Meta-sgd: Learning to learn quickly for few shot learning
Li, Z., Zhou, F., Chen, F., Li, H.: · 2017
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Articulated pose estimation with tiny synthetic videos
Park, D., Ramanan, D.: · 2015
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Ontological supervision for fine grained classification of street view storefronts
Movshovitz-Attias, Y., Yu, Q., Stumpe, M., Shet, V., Arnoud, S., Yatziv, L.: · 2015
Cited alongside, same era.
Learning to generate chairs with convolutional neural networks
Dosovitskiy, A., Springenberg, J., Brox, T.: · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Predicting deep zero-shot convolutional neural networks using textual descriptions
Ba, J.L., Swersky, K., Fidler, S., Salakhutdinov, R.: · 2015
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render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views
Su, H., Qi, C.R., Li, Y., Guibas, L.J.: · 2015
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Optimization as a model for few-shot learning
Ravi, S., Larochelle, H.: · 2017
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Meta networks
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Model-agnostic meta-learning for fast adaptation of deep networks
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Multi-attention network for one shot learning
Wang, P., Liu, L., Shen, C., Huang, Z., Hengel, A., Tao Shen, H.: · 2017
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Low-shot visual recognition by shrinking and hallucinating features
Hariharan, B., Girshick, R.: · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: · 2017
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Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z.: · 2017
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Generative multi-adversarial networks
Durugkar, I., Gemp, I., Mahadevan, S.: · 2017
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Stacked generative adversarial networks
Huang, X., Li, Y., Poursaeed, O., Hopcroft, J., Belongie, S.: · 2017
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One-shot learning for semantic segmentation
Shaban, A., Bansal, S., Liu, Z., Essa, I., Boots, B.: · 2017
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One-shot video object segmentation
Caelles, S., Maninis, K.K., Pont-Tuset, J., Leal-Taixe, L., Cremers, D., Gool, L.V.: · 2017
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Deep meta-learning: Learning to learn in the concept space
Zhou, F., Wu, B., Li, Z.: · 2018
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On first-order meta-learning algorithms
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Few-Shot Image Recognition by Predicting Parameters from Activations
Qiao, S., Liu, C., Shen, W., Yuille, A.L.: · 2018
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Learning to compare: Relation network for few-shot learning
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Few-Shot Learning with Metric-Agnostic Conditional Embeddings
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Dynamic Few-Shot Visual Learning without Forgetting
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Memory Matching Networks for One-Shot Image Recognition
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Few-shot learning with graph neural networks
Garcia, V., Bruna, J.: · 2018
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Delta-encoder: an effective sample synthesis method for few-shot object recognition
Schwartz, E., Karlinsky, L., Shtok, J., Harary, S., Marder, M., Feris, R., Kumar, A., Giryes, R., Bronstein, A.M.: · 2018
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Low-Shot Learning from Imaginary Data
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Feature space transfer for data augmentation
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