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Found 6 papers, 5 papers with code
Open-Set Likelihood Maximization for Few-Shot Learning
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i. e. classifying instances among a set of classes for which we only have a few labeled samples, while simultaneously detecting instances that do not belong to any known class.
Model-Agnostic Few-Shot Open-Set Recognition
Through extensive experiments spanning 5 datasets, we show that OSTIM surpasses both inductive and existing transductive methods in detecting open-set instances while competing with the strongest transductive methods in classifying closed-set instances.
Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling
Every day, a new method is published to tackle Few-Shot Image Classification, showing better and better performances on academic benchmarks.
Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift
To classify query instances from novel classes encountered at test-time, they only require a support set composed of a few labelled samples.
Learning to Communicate in Multi-Agent Reinforcement Learning : A Review
We consider the issue of multiple agents learning to communicate through reinforcement learning within partially observable environments, with a focus on information asymmetry in the second part of our work.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Meta-learning algorithms for Few-Shot Computer Vision
Few-Shot Learning is the challenge of training a model with only a small amount of data.
