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Oct 13, 2021 · Transfer learning hopes to borrow transferable knowledge from source domain (related domain) to build up an adapter for target domain.
To further improve the performance of the adapter, in this paper, we propose a parallel ensemble strategy based on evidence theory. Specifically, firstly, we ...
Instance-based transfer learning methods utilize labeled examples from one domain to improve learning performance in another domain via knowledge transfer.
Missing: Evidence Theory.
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[PDF] Ensemble of Anchor Adapters for Transfer Learning - CUHK CSE
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Oct 28, 2016 · Aiming at more robust transfer learning models, we propose an ENsemble framework of anCHOR adapters (ENCHOR for short), in which an anchor ...
Missing: Evidence | Show results with:Evidence
An ENsemble framework of anCHOR adapters (ENCHOR for short), in which an anchor adapter adapts the features of instances based on their similarities to a ...
Missing: Evidence Theory.
In transfer learning the lack of labelled information from the target domain is compensated by the availability at training time of a set of unlabelled examples ...
Aiming at more robust transfer learning models, we propose an ENsemble framework of anCHOR adapters (ENCHOR for short), in which an anchor adapter adapts the ...
This repository collects important tools and papers related to adapter methods for recent large pre-trained neural networks.
Missing: Evidence | Show results with:Evidence
The framework of the method is mainly composed of three parts: construction of anchor adapters, deep domain adaptation transfer learning and ensemble of anchor ...
Missing: Evidence Theory.
A widely adopted approach for mitigating overconfidence in deep learning is to make predictions using an ensemble of neural networks rather than a single model.