← Learning Types
Machine Learning › Learning Types ›

Few-Shot Learning

1062 directly classified papers

Papers per year

Papers

Learning Hawkes Processes from a handful of events NIPS 2019
Low Shot Box Correction for Weakly Supervised Object Detection IJCAI 2019
Matching the Blanks: Distributional Similarity for Relation Learning ACL 2019
Multi-Level Matching and Aggregation Network for Few-Shot Relation Classification ACL 2019
Few-shot Video-to-Video Synthesis NIPS 2019
Conditional Neural Movement Primitives RSS 2019
Combining Unsupervised Pre-training and Annotator Rationales to Improve Low-shot Text Classification IJCNLP 2019
Extreme Multi-Label Legal Text Classification: A Case Study in EU Legislation NAACL 2019
Reevaluating Argument Component Extraction in Low Resource Settings EMNLP 2019
Domain Adaptive Dialog Generation via Meta Learning ACL 2019
Compositional Generalization for Primitive Substitutions IJCNLP 2019
Answering Conversational Questions on Structured Data without Logical Forms EMNLP 2019
Learning to Write Stylized Chinese Characters by Reading a Handful of Examples IJCAI 2018
MetaGAN: An Adversarial Approach to Few-Shot Learning NIPS 2018
Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning CVPR 2018
Learning Deep Disentangled Embeddings With the F-Statistic Loss NIPS 2018
Task-Embedded Control Networks for Few-Shot Imitation Learning CORL 2018
Low-Shot Learning From Imaginary Data CVPR 2018
Towards one-shot learning for rare-word translation with external experts ACL 2018
The INaturalist Species Classification and Detection Dataset CVPR 2018
Few-Shot Charge Prediction with Discriminative Legal Attributes COLING 2018
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace ICML 2018
CRUISE: Cold-Start New Skill Development via Iterative Utterance Generation ACL 2018
Learning Structure and Strength of CNN Filters for Small Sample Size Training CVPR 2018
Multi-Content GAN for Few-Shot Font Style Transfer CVPR 2018