← Learning Types
Machine Learning › Learning Types ›

Few-Shot Learning

1062 directly classified papers

Papers per year

Papers

Few-shot Font Generation with Localized Style Representations and Factorization AAAI 2021
Continuous Coordination As a Realistic Scenario for Lifelong Learning ICML 2021
Few-shot Language Coordination by Modeling Theory of Mind ICML 2021
ESC: Redesigning WSD with Extractive Sense Comprehension NAACL 2021
How many data points is a prompt worth? NAACL 2021
A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios NAACL 2021
Learning How to Ask: Querying LMs with Mixtures of Soft Prompts NAACL 2021
Towards Fair Cross-Domain Adaptation via Generative Learning WACV 2021
Adaptive-Attentive Geolocalization From Few Queries: A Hybrid Approach WACV 2021
Deep Template-Based Object Instance Detection WACV 2021
Variational Metric Scaling for Metric-Based Meta-Learning AAAI 2020
Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations COLING 2020
Instance Credibility Inference for Few-Shot Learning CVPR 2020
Make One-Shot Video Object Segmentation Efficient Again NIPS 2020
Few-Shot Video Classification via Temporal Alignment CVPR 2020
Incremental Few-Shot Object Detection CVPR 2020
Meta-Learning of Neural Architectures for Few-Shot Learning CVPR 2020
Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition CVPR 2020
Action Genome: Actions As Compositions of Spatio-Temporal Scene Graphs CVPR 2020
Unsupervised Representation Learning for Gaze Estimation CVPR 2020
Turning 30: New Ideas in Inductive Logic Programming IJCAI 2020
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL NIPS 2020
Auxiliary Task Reweighting for Minimum-data Learning NIPS 2020
Intent Detection with WikiHow AACL 2020
Reed at SemEval-2020 Task 9: Fine-Tuning and Bag-of-Words Approaches to Code-Mixed Sentiment Analysis SEMEVAL 2020