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Found 4 papers, 2 papers with code
Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty
In this paper, we propose a novel framework called Hyper-Evidential Neural Network (HENN) that explicitly models predictive uncertainty due to composite class labels in training data in the context of the belief theory called Subjective Logic (SL).
PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information
Meta learning has proven to be able to learn a parametrized model for FSC by training on various other classification tasks.
A Nested Bi-level Optimization Framework for Robust Few Shot Learning
Model-Agnostic Meta-Learning (MAML), a popular gradient-based meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal.
Fair Meta-Learning For Few-Shot Classification
Artificial intelligence nowadays plays an increasingly prominent role in our life since decisions that were once made by humans are now delegated to automated systems.
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