scholar.google.com › citations
This paper presents a few-shot AMC approach that integrates signal transformation and meta-learning. The former enhances class separability, while the latter ...
People also ask
What is the role of meta-learning for few-shot learning?
What is few-shot learning?
What is few-shot learning algorithms in NLP?
What is the difference between meta-learning and transfer learning?
In this paper, a novel FSL framework called Attention Relation Network (ARN) is proposed, which introduces channel and spatial attention respectively to learn a ...
Missing: Meta- | Show results with:Meta-
May 17, 2023 · To address this issue, we propose a method for few-shot learning with fewer tasks, which we call MetaModulation. The key idea is to use a neural ...
Missing: Recognition Transformation
Apr 7, 2021 · Zhou et al. [15] introduced AMCRN, an architecture based on CNN that assesses feature similarity between test data and annotated few-shot data.
Feb 16, 2024 · This approach encourages the model to develop a flexible understanding of class characteristics and apply this knowledge to new classes it faces ...
Abstract. Meta-learning algorithms are able to learn a new task using previously learned knowledge, but they often require a large number of meta-training.
Missing: Recognition | Show results with:Recognition
Section 4 summarizes the existing FSCIL approaches, including traditional machine learning methods, meta learning-based methods, feature and feature space-based ...
Section 4 summarizes the ex- isting FSCIL approaches, including traditional machine learn- ing methods, meta learning-based methods, feature and feature space- ...
Feb 17, 2024 · In this paper, inspired by the fact that the energy distribution of the signal of interest is always concentrated in time-frequency images, a ...
Feb 22, 2023 · In this article, a new meta-learning method is proposed for a few-shot AMC with distribution bias. ... features, which can efficiently identify ...