Jul 31, 2021 · The task becomes even more challenging if it is also required to identify samples from unknown categories (open-set classification). Learning a ...
Abstract—In many applications, we are constrained to learn classifiers from very limited data (few-shot classification).
People also ask
What is Meta-learning and few-shot learning?
How do you implement few-shot learning in NLP?
Existing few-shot open-set recognition (FSOSR) methods rely on thresholding schemes, with some considering uniform probability for open-class samples. However, ...
This work suggests the use of meta-learning techniques, commonly used for few-shot classification, for the solution of open-set recognition, and introduces ...
To address these concerns, we propose Reconstructing Exemplar-based Few-shot Open-set ClaSsifier (ReFOCS). By using a novel exemplar reconstruction-based meta- ...
This combines the random selection of a set of novel classes per episode, a loss that maximizes the posterior entropy for ex- amples of those classes, and a new ...
Missing: Reconstruction Guided
Recent advancements in prototype-based Few-Shot Open-Set Recognition (FSOSR) approaches reject outliers based on the high metric distances from the known ...
Sep 29, 2023 · The task becomes even more challenging if it is also required to identify samples from unknown categories (open-set classification). Learning a ...
This paper provides a comprehensive survey of existing open set recognition techniques covering various aspects ranging from related definitions, ...
Meta Evidential Transformer for Few-Shot Open-Set Recognition. Opponent class facilitated model training. Training with evidence-guided open-set loss. Support ...