Jan 28, 2022 · This paper proposes a framework, terms as ConFeSS, for dealing with cross-domain few-shot learning problems. Specifically, it firstly trains a ...
This paper proposes a novel contrastive learning and feature selection system (ConFeSS) for single- source cross-domain few-shot learning. Our framework ...
In this paper, we propose a framework for few-shot learning coined as ConFeSS (Contrastive Learning and Feature Selection System) that tackles large domain ...
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In this paper, we propose a framework for few-shot learning coined as ConFeSS (Contrastive Learning and Feature Selection System) that tackles large domain ...
In this paper, we propose a novel framework, MemREIN, which considers Memorized, Restitution, and Instance Normalization for cross-domain few-shot learning.
ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning , by Debasmit Das, Sungrack Yun and Fatih Porikli [bib]. das2022confess. Hierarchical ...
This work proposes Cross-domain Hebbian Ensemble Few-shot learning (CHEF), which achieves representation fusion by an ensemble of Hebbians acting on ...
Jun 24, 2024 · Confess: A framework for single source cross- domain few-shot learning. In International Conference on. Learning Representations, 2022. [Du ...
This paper proposes a simple yet powerful method that re-randomizes the parameters fitted on the source domain before adapting to the target data.
Toward this issue, we propose to address the cross-domain few-shot learning problem where only extremely few samples are available in target domains. Under this ...