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few-shot learning

3390 papers

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Also known as

FSL FEW-SHOT

Co-occurring keywords

large language model (12755) zero-shot learning (3637) transfer learning (5442) in-context learning (1369) text classification (6776) domain adaptation (4578) prompt engineering (1128) language model (4573) representation learning (6174) contrastive learning (3979)

Papers

MetaGAN: An Adversarial Approach to Few-Shot Learning NIPS 2018
Delta-encoder: an effective sample synthesis method for few-shot object recognition NIPS 2018
Bayesian Model-Agnostic Meta-Learning NIPS 2018
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation EMNLP 2018
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning COLING 2018
Dynamic Few-Shot Visual Learning Without Forgetting CVPR 2018
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning NIPS 2018
Neural Voice Cloning with a Few Samples NIPS 2018
Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces EMNLP 2018
Multi-Attention Network for One Shot Learning CVPR 2017
Domain Adaptation by Mixture of Alignments of Second- or Higher-Order Scatter Tensors CVPR 2017
Prototypical Networks for Few-shot Learning NIPS 2017
Meta Networks ICML 2017
Variational Memory Addressing in Generative Models NIPS 2017
Few-Shot Object Recognition From Machine-Labeled Web Images CVPR 2017
High-risk learning: acquiring new word vectors from tiny data EMNLP 2017
LCNN: Lookup-Based Convolutional Neural Network CVPR 2017
Fine-Grained Recognition of Thousands of Object Categories With Single-Example Training CVPR 2017
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks ICML 2017
Learning to Model the Tail NIPS 2017
How Robots Learn to Classify New Objects Trained from Small Data Sets CORL 2017
Neural Program Meta-Induction NIPS 2017
Few-Shot Adversarial Domain Adaptation NIPS 2017
Meta-Learning with Memory-Augmented Neural Networks ICML 2016
Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs NIPS 2016