E. Bart and S. Ullman. paper, Active one-shot learning, arXiv preprint, 2017. W. Liu, C. Zhang, G. Lin, and F. Liu. Then, meta-transfer learning learns a good representation \theta_M for super-resolution tasks with diverse blur kernel scenarios. I’m editing for the first time and scared of making mistakes. code, Meta-World: A benchmark and evaluation for multi-task and meta reinforcement learning, arXiv preprint, 2019. F. Zhao, J. Zhao, S. Yan, and J. Feng. paper X. Li, T. Wei, Y. P. Chen, Y.-W. Tai, and C.-K. Tang. Q. Cai, Y. Pan, T. Yao, C. Yan, and T. Mei. paper, SimPropNet: Improved similarity propagation for few-shot image segmentation, in IJCAI, 2020. paper, Neural voice cloning with a few samples, in NeurIPS, 2018. Ł. Kaiser, O. Nachum, A. Roy, and S. Bengio. paper, Boosting few-shot learning with adaptive margin loss, in CVPR, 2020. L. Fei-Fei, R. Fergus, and P. Perona.
paper, Low-shot learning via covariance-preserving adversarial augmentation networks, in NeurIPS, 2018. Liu, A. Tao, G. Liu, J. Kautz, and B. Catanzaro.
Y. Wang, C. Xu, C. Liu, L. Zhang, and Y. Fu. Another problem is that they are applicable only to the specific condition of data that they are supervised. J. Lee, D. Ramanan, and R. Girdhar. paper, Learning to remember rare events, in ICLR, 2017. B. Hariharan and R. Girshick. T. Gao, X. Han, Z. Liu, and M. Sun. Song. R. Kwitt, S. Hegenbart, and M. Niethammer. code, Dense classification and implanting for few-shot learning, in CVPR, 2019. T. Gao, X. Han, H. Zhu, Z. Liu, P. Li, M. Sun, and J. Zhou. We use essential cookies to perform essential website functions, e.g. T. Wang, X. Zhang, L. Yuan, and J. Feng G. Denevi, C. Ciliberto, D. Stamos, and M. Pontil. He. with group normalization and weight standardization, Discover more papers related to the topics discussed in this paper, On Robustness and Transferability of Convolutional Neural Networks, Estimating the Brittleness of AI: Safety Integrity Levels and the Need for Testing Out-Of-Distribution Performance, A Principled Approach to Data Valuation for Federated Learning, Deep Neural Networks with Short Circuits for Improved Gradient Learning, Deep Convolutional Neural Networks for Unconstrained Ear Recognition, Scalable Transfer Learning with Expert Models, Interactive Collaborative Robotics: 5th International Conference, ICR 2020, St Petersburg, Russia, October 7-9, 2020, Proceedings, A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark, Exploring the Limits of Weakly Supervised Pretraining, Explicit Inductive Bias for Transfer Learning with Convolutional Networks, Rethinking the Inception Architecture for Computer Vision, Prototypical Networks for Few-shot Learning, EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, Learning to Compare: Relation Network for Few-Shot Learning, Revisiting Unreasonable Effectiveness of Data in Deep Learning Era, Blog posts, news articles and tweet counts and IDs sourced by, arXiv: Computer Vision and Pattern Recognition, Hartmut Maennel, Ibrahim M. Alabdulmohsin, … Daniel Keysers, Arijit Sehanobish, H. H. Corzo, … D. V. Dijk, Janis Klaise, Arnaud Van Looveren, … Alexandru Coca, View 11 excerpts, cites background, results and methods, View 4 excerpts, cites background and methods, By clicking accept or continuing to use the site, you agree to the terms outlined in our, Open-Sourcing BiT: Exploring Large-Scale Pre-training for Computer Vision. H.-J. Sun, Y. Liu, T.-S. Chua, and B. Schiele. paper, Few-shot human motion prediction via meta-learning, in ECCV, 2018. paper, Infinite mixture prototypes for few-shot learning, in ICML, 2019. Glass, and J. Tenenbaum. paper, MetaGAN: An adversarial approach to few-shot learning, in NeurIPS, 2018. L. Yang, L. Li, Z. Zhang, X. Zhou, E. Zhou, and Y. Liu. A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby Y. Zhang, H. Tang, and K. Jia. This page shows tables extracted from arXiv papers on the left-hand side. On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class. code, Variational few-shot learning, in ICCV, 2019. code, Few-shot text classification with distributional signatures, in ICLR, 2020. code, Low-shot learning with imprinted weights, in CVPR, 2018. We scale up pre-training, and create a simple recipe that we call Big Transfer (BiT). Z. Chen, Y. Fu, Y.-X. S. H. Mohammadi and T. Kim. Convolutional neural networks (CNNs) have shown dramatic improvements in single image super-resolution (SISR) by using large-scale external samples. N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel.
paper Some features of the site may not work correctly. paper, Gradient-based meta-learning with learned layerwise metric and subspace, in ICML, 2018. paper, Weakly supervised few-shot object segmentation using co-attention with visual and semantic embeddings, in IJCAI, 2020. updated with the latest ranking of this If nothing happens, download the GitHub extension for Visual Studio and try again. paper A. Rusu, R. Pascanu, F. Visin, H. Yin, and R. Hadsell. paper, Low-shot learning with large-scale diffusion, in CVPR, 2018. code, Large-scale few-shot learning: Knowledge transfer with class hierarchy, in CVPR, 2019.
In general, you should always think of transfer learning as a general concept or principle, where we will try to solve a target task using source task-domain knowledge.
Click on a cell in a table on the left hand side where the result comes from. code, Few-shot generalization for single-image 3D reconstruction via priors, in ICCV, 2019. paper, Collect and select: Semantic alignment metric learning for few-shot learning, in ICCV, 2019. paper, Few-shot open-set recognition using meta-learning, in CVPR, 2020. paper, Learning from small sample sets by combining unsupervised meta-training with CNNs, in NeurIPS, 2016. paper, Extending a parser to distant domains using a few dozen partially annotated examples, in ACL, 2018. paper, Watch, try, learn: Meta-learning from demonstrations and rewards, in ICLR, 2020. paper, Meta-learning for semi- supervised few-shot classification, in ICLR, 2018.
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