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SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
Wang, Y.; Chao, W.-L.; Weinberger, K. Q.; and van der Maaten, L. 2019 · 1911
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VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY
BRIER, G. W. 1950 · 1950
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A Cluster Separation Measure
Davies, D. L.; and Bouldin, D. W. 1979 · 1979
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Learning by transduction, vol UAI’98
Gammerman, A.; Vovk, V.; and Vapnik, V. 1998 · 1998
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Cross-domain few-shot classification via learned feature-wise transformation
Tseng, H.-Y.; Lee, H.-Y.; Huang, J.-B.; and Yang, M.-H. 2020 · 2001
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Estimation of Dependences Based on Empirical Data
Vapnik, V. N. 2006 · 2006
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Caltech-UCSD Birds 200
Welinder, P.; Branson, S.; Mita, T.; Wah, C.; Schroff, F.; Belongie, S.; and Perona, P. 2010 · 2010
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Semi-supervised Learning with Deep Generative Models
Kingma, D. P.; Mohamed, S.; Jimenez Rezende, D.; and Welling, M. 2014 · 2014
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Auto-Encoding Variational Bayes
Kingma, D. P.; and Welling, M. 2014 · 2014
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S.; and Szegedy, C. 2015 · 2015
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Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2015 · 2015
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Matching Networks for One Shot Learning
Vinyals, O.; Blundell, C.; Lillicrap, T.; kavukcuoglu, k.; and Wierstra, D. 2016 · 2016
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Edwards, H.; and Storkey, A. J. 2017 · 2017
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Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C.; Abbeel, P.; and Levine, S. 2017 · 2017
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On Calibration of Modern Neural Networks
Guo, C.; Pleiss, G.; Sun, Y.; and Weinberger, K. Q. 2017 · 2017
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beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Higgins, I.; Matthey, L.; Pal, A.; Burgess, C.; Glorot, X.; Botvinick, M.; Mohamed, S.; and Lerchner, A. 2017 · 2017
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Optimization as a Model for Few-Shot Learning
Ravi, S.; and Larochelle, H. 2017 · 2017
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Prototypical Networks for Few-shot Learning
Snell, J.; Swersky, K.; and Zemel, R. 2017 · 2017
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Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L. u.; and Polosukhin, I. 2017 · 2017
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Learning Disentangled Joint Continuous and Discrete Representations
Dupont, E. 2018 · 2018
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Probabilistic Model-Agnostic Meta-Learning
Finn, C.; Xu, K.; and Levine, S. 2018 · 2018
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Conditional Adversarial Domain Adaptation
Long, M.; CAO, Z.; Wang, J.; and Jordan, M. I. 2018 · 2018
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Umap: Uniform manifold approximation and projection for dimension reduction
McInnes, L.; Healy, J.; and Melville, J. 2018 · 2018
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A Simple Neural Attentive Meta-Learner
Mishra, N.; Rohaninejad, M.; Chen, X.; and Abbeel, P. 2018 · 2018
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TADAM: Task dependent adaptive metric for improved few-shot learning
Oreshkin, B.; Rodríguez López, P.; and Lacoste, A. 2018 · 2018
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Meta-Learning for Semi-Supervised Few-Shot Classification
Ren, M.; Ravi, S.; Triantafillou, E.; Snell, J.; Swersky, K.; Tenenbaum, J. B.; Larochelle, H.; and Zemel, R. S. 2018 · 2018
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Few-Shot Learning with Graph Neural Networks
Satorras, V. G.; and Estrach, J. B. 2018 · 2018
Cited alongside, same era.
Learning to Compare: Relation Network for Few-Shot Learning
Sung, F.; Yang, Y.; Zhang, L.; Xiang, T.; Torr, P. H.; and Hospedales, T. M. 2018 · 2018
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Bayesian Model-Agnostic Meta-Learning
Yoon, J.; Kim, T.; Dia, O.; Kim, S.; Bengio, Y.; and Ahn, S. 2018 · 2018
Information Maximization for Few-Shot Learning
Boudiaf, M.; Ziko, I.; Rony, J.; Dolz, J.; Piantanida, P.; and Ben Ayed, I. 2020 · 2020
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A Baseline for Few-Shot Image Classification
Dhillon, G. S.; Chaudhari, P.; Ravichandran, A.; and Soatto, S. 2020 · 2020
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Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
Galy-Fajou, T.; Wenzel, F.; Donner, C.; and Opper, M. 2020 · 2020
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Empirical Bayes Transductive Meta-Learning with Synthetic Gradients
Hu, S. X.; Moreno, P.; Xiao, Y.; Shen, X.; Obozinski, G.; Lawrence, N.; and Damianou, A. 2020 · 2020
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Meta-Learning with Shared Amortized Variational Inference
Iakovleva, E.; Verbeek, J.; and Alahari, K. 2020 · 2020
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Prototype Rectification for Few-Shot Learning
Liu, J.; Song, L.; and Qin, Y. 2020 · 2020
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Cited alongside, same era.
How to train your MAML
Antreas Antoniou; Harrison Edwards; and Amos J. Storkey. 2019 · 2019
Cited alongside, same era.
A Closer Look at Few-shot Classification
Chen, W.-Y.; Liu, Y.-C.; Kira, Z.; Wang, Y.-C.; and Huang, J.-B. 2019 · 2019
Cited alongside, same era.
Meta-Learning Probabilistic Inference for Prediction
Gordon, J.; Bronskill, J.; Bauer, M.; Nowozin, S.; and Turner, R. 2019 · 2019
Cited alongside, same era.
Cross Attention Network for Few-shot Classification
Hou, R.; Chang, H.; MA, B.; Shan, S.; and Chen, X. 2019 · 2019
Cited alongside, same era.
Meta-Learning with Differentiable Convex Optimization
Lee, K.; Maji, S.; Ravichandran, A.; and Soatto, S. 2019 · 2019
Cited alongside, same era.
Finding Task-Relevant Features for Few-Shot Learning by Category Traversal
Li, H.; Eigen, D.; Dodge, S.; Zeiler, M.; and Wang, X. 2019 · 2019
Cited alongside, same era.
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Charting the right manifold: Manifold mixup for few-shot learning
Mangla, P.; Kumari, N.; Sinha, A.; Singh, M.; Krishnamurthy, B.; and Balasubramanian, V. N. 2020 · 2020
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Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
Patacchiola, M.; Turner, J.; Crowley, E. J.; O’Boyle, M. F. P.; and Storkey, A. J. 2020 · 2020
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DPGN: Distribution Propagation Graph Network for Few-Shot Learning
Yang, L.; Li, L.; Zhang, Z.; Zhou, X.; Zhou, E.; and Liu, Y. 2020 · 2020
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Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions
Ye, H.-J.; Hu, H.; Zhan, D.-C.; and Sha, F. 2020 · 2020
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Laplacian Regularized Few-Shot Learning
Ziko, I. M.; Dolz, J.; Granger, E.; and Ayed, I. B. 2020 · 2020
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Leveraging the feature distribution in transfer-based few-shot learning
Hu, Y.; Gripon, V.; and Pateux, S. 2021 · 2021
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Capturing Label Characteristics in {VAE}s
Joy, T.; Schmon, S.; Torr, P.; N, S.; and Rainforth, T. 2021 · 2021
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Learning Intact Features by Erasing-Inpainting for Few-shot Classification
Li, J.; Wang, Z.; and Hu, X. 2021 · 2021
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Partner-assisted learning for few-shot image classification
Ma, J.; Xie, H.; Han, G.; Chang, S.-F.; Galstyan, A.; and Abd-Almageed, W. 2021 · 2021
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BOIL: Towards Representation Change for Few-shot Learning
Oh, J.; Yoo, H.; Kim, C.; and Yun, S.-Y. 2021 · 2021
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Re-ranking for image retrieval and transductive few-shot classification
SHEN, X.; Xiao, Y.; Hu, S. X.; Sbai, O.; and Aubry, M. 2021 · 2021
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Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes
Snell, J.; and Zemel, R. 2021 · 2021
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Amortized Bayesian Prototype Meta-learning: A New Probabilistic Meta-learning Approach to Few-shot Image Classification
Sun, Z.; Wu, J.; Li, X.; Yang, W.; and Xue, J.-H. 2021 · 2021
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Few-Shot Classification With Feature Map Reconstruction Networks
Wertheimer, D.; Tang, L.; and Hariharan, B. 2021 · 2021
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Task-Aware Part Mining Network for Few-Shot Learning
Wu, J.; Zhang, T.; Zhang, Y.; and Wu, F. 2021 · 2021
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Attentional Constellation Nets for Few-Shot Learning
Xu, W.; yifan xu; Wang, H.; and Tu, Z. 2021 · 2021
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Enhancing Few-Shot Image Classification With Unlabelled Examples
Bateni, P.; Barber, J.; van de Meent, J.-W.; and Wood, F. 2022 · 2022
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Squeezing backbone feature distributions to the max for efficient few-shot learning
Hu, Y.; Pateux, S.; and Gripon, V. 2022 · 2022
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