暂无分享,去创建一个
Sebastian Nowozin | Richard E. Turner | Massimiliano Patacchiola | Katja Hofmann | Daniela Massiceti | John Bronskill | S. Nowozin | Massimiliano Patacchiola | Katja Hofmann | J. Bronskill | Daniela Massiceti | Sebastian Nowozin
[1] Aaron C. Courville,et al. FiLM: Visual Reasoning with a General Conditioning Layer , 2017, AAAI.
[2] Richard S. Zemel,et al. Prototypical Networks for Few-shot Learning , 2017, NIPS.
[3] Amos J. Storkey,et al. How to train your MAML , 2018, ICLR.
[4] Leonid Sigal,et al. Improved Few-Shot Visual Classification , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[6] Sebastian Nowozin,et al. Meta-Learning Probabilistic Inference for Prediction , 2018, ICLR.
[7] Sergey Levine,et al. Meta-Learning with Implicit Gradients , 2019, NeurIPS.
[8] Katja Hofmann,et al. Fast Context Adaptation via Meta-Learning , 2018, ICML.
[9] Chen Sun,et al. Revisiting Unreasonable Effectiveness of Data in Deep Learning Era , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[10] Xiaohua Zhai,et al. A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark , 2019 .
[11] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[12] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Hugo Larochelle,et al. Optimization as a Model for Few-Shot Learning , 2016, ICLR.
[14] Edward Cutrell,et al. ORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition , 2021, ArXiv.
[15] Mark B. Ring. CHILD: A First Step Towards Continual Learning , 1997, Machine Learning.
[16] Amos Storkey,et al. Meta-Learning in Neural Networks: A Survey , 2020, IEEE transactions on pattern analysis and machine intelligence.
[17] Joshua Achiam,et al. On First-Order Meta-Learning Algorithms , 2018, ArXiv.
[18] Victor Lempitsky,et al. Few-Shot Adversarial Learning of Realistic Neural Talking Head Models , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[19] Michael Biehl,et al. On-line Learning in Neural Networks , 1998 .
[20] Sebastian Nowozin,et al. Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes , 2019, NeurIPS.
[21] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[22] Julien Mairal,et al. Selecting Relevant Features from a Multi-domain Representation for Few-Shot Classification , 2020, ECCV.
[23] Sergey Levine,et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks , 2017, ICML.
[24] P. Mahalanobis. On the generalized distance in statistics , 1936 .
[25] Sung Ju Hwang,et al. Large-Scale Meta-Learning with Continual Trajectory Shifting , 2021, ICML.
[26] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[27] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[28] Quoc V. Le,et al. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.
[29] Steven C. H. Hoi,et al. Online Learning: A Comprehensive Survey , 2018, Neurocomputing.
[30] Tianqi Chen,et al. Training Deep Nets with Sublinear Memory Cost , 2016, ArXiv.
[31] Heike Freud,et al. On Line Learning In Neural Networks , 2016 .
[32] Oriol Vinyals,et al. Matching Networks for One Shot Learning , 2016, NIPS.
[33] Lucas Beyer,et al. Big Transfer (BiT): General Visual Representation Learning , 2020, ECCV.
[34] Amos Storkey,et al. Defining Benchmarks for Continual Few-Shot Learning , 2020, ArXiv.
[35] Andrew Zisserman,et al. CrossTransformers: spatially-aware few-shot transfer , 2020, NeurIPS.
[36] Hugo Larochelle,et al. Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples , 2019, ICLR.
[37] Xiaohua Zhai,et al. Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark , 2021, ArXiv.