Oct 9, 2021 · In this work, we investigate the unexplored intersection of domain generalization (DG) and data-free learning.
Data-Free Domain Generalization (DFDG) is a problem setting that assumes only access to models trained on the source domains, without requir- ing data from ...
In this work, we investigate the unexplored intersection of domain generalization and data-free learning. In particular, we address the question: How can ...
Mar 8, 2024 · We introduce a novel approach to domain extrapolation that leverages reasoning ability and the extensive knowledge encapsulated within large language models ( ...
This work proposes DEKAN, an approach that extracts and fuses domain-specific knowledge from the available teacher models into a student model robust to ...
We propose DEKAN, an approach that extracts and fuses domain-specific knowledge from the available teacher models into a student model robust to domain shift.
In this work, we investigate the unexplored intersection of domain generalization (DG) and data-free learning.
Dec 6, 2021 · A multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster ...
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This work introduces a novel approach to domain extrapolation that leverages reasoning ability and the extensive knowledge encapsulated within large ...
Apr 24, 2023 · We propose a Distribution Free Domain Generalization (DFDG) procedure for classification by conducting standardization to avoid the dominance of a few domains.