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[1] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[2] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] Jing Zhang,et al. Few-Shot Learning via Saliency-Guided Hallucination of Samples , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[4] C A Nelson,et al. Learning to Learn , 2017, Encyclopedia of Machine Learning and Data Mining.
[5] Martial Hebert,et al. Image Deformation Meta-Networks for One-Shot Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Jinlu Liu,et al. Generalized Adaptation for Few-Shot Learning , 2019 .
[7] Gregory R. Koch,et al. Siamese Neural Networks for One-Shot Image Recognition , 2015 .
[8] O. Chapelle,et al. Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews] , 2009, IEEE Transactions on Neural Networks.
[9] Taesup Kim,et al. Edge-Labeling Graph Neural Network for Few-Shot Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[11] Yu-Chiang Frank Wang,et al. A Closer Look at Few-shot Classification , 2019, ICLR.
[12] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[13] Emilio Soria Olivas,et al. Handbook of Research on Machine Learning Applications and Trends : Algorithms , Methods , and Techniques , 2009 .
[14] Quanming Yao,et al. Few-shot Learning: A Survey , 2019, ArXiv.
[15] Xiaogang Wang,et al. Finding Task-Relevant Features for Few-Shot Learning by Category Traversal , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Jinlu Liu,et al. Prototype Rectification for Few-Shot Learning , 2020, ECCV.
[17] Nikos Komodakis,et al. Wide Residual Networks , 2016, BMVC.
[18] Ioannis Mitliagkas,et al. Manifold Mixup: Better Representations by Interpolating Hidden States , 2018, ICML.
[19] Hugo Larochelle,et al. Optimization as a Model for Few-Shot Learning , 2016, ICLR.
[20] Debasmit Das,et al. A Two-Stage Approach to Few-Shot Learning for Image Recognition , 2019, IEEE Transactions on Image Processing.
[21] Oriol Vinyals,et al. Matching Networks for One Shot Learning , 2016, NIPS.
[22] Yan Wang,et al. SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning , 2019, ArXiv.
[23] Pietro Perona,et al. The Caltech-UCSD Birds-200-2011 Dataset , 2011 .
[24] Nikos Komodakis,et al. Unsupervised Representation Learning by Predicting Image Rotations , 2018, ICLR.
[25] Abhishek Sinha,et al. Charting the Right Manifold: Manifold Mixup for Few-shot Learning , 2019, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).
[26] Richard S. Zemel,et al. Prototypical Networks for Few-shot Learning , 2017, NIPS.
[27] F. Scarselli,et al. A new model for learning in graph domains , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[28] Subhransu Maji,et al. Meta-Learning With Differentiable Convex Optimization , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Kilian Q. Weinberger,et al. Simplifying Graph Convolutional Networks , 2019, ICML.
[30] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[31] Jinlu Liu,et al. Fast and Generalized Adaptation for Few-Shot Learning , 2019, ArXiv.
[32] Razvan Pascanu,et al. Meta-Learning with Latent Embedding Optimization , 2018, ICLR.
[33] Quoc V. Le,et al. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.
[34] James T. Kwok,et al. Generalizing from a Few Examples , 2019, ACM Comput. Surv..
[35] Nikos Komodakis,et al. Generating Classification Weights With GNN Denoising Autoencoders for Few-Shot Learning , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Fei Sha,et al. Learning Embedding Adaptation for Few-Shot Learning , 2018, ArXiv.
[37] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[38] Gavriel Salomon,et al. T RANSFER OF LEARNING , 1992 .
[39] Tao Xiang,et al. Learning to Compare: Relation Network for Few-Shot Learning , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[40] Joan Bruna,et al. Few-Shot Learning with Graph Neural Networks , 2017, ICLR.
[41] Gabriela Csurka,et al. Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost , 2012, ECCV.
[42] Eunho Yang,et al. Learning to Propagate Labels: Transductive Propagation Network for Few-Shot Learning , 2018, ICLR.
[43] Luis Perez,et al. The Effectiveness of Data Augmentation in Image Classification using Deep Learning , 2017, ArXiv.
[44] Luca Bertinetto,et al. Meta-learning with differentiable closed-form solvers , 2018, ICLR.