We propose a novel unsupervised transfer learning framework that utilises unlabelled auxiliary data to quantify and select the most relevant transferrable ...
We propose a novel unsupervised transfer learning frame- work that utilises unlabelled auxiliary data to quantify and select the most relevant transferrable ...
Abstract. We propose a novel unsupervised transfer learning frame- work that utilises unlabelled auxiliary data to quantify and select the.
We propose a novel unsupervised transfer learning framework that utilises unlabelled auxiliary data to quantify and select the most relevant transferrable ...
Unsupervised Selective Transfer Learning for Object Recognition. https://doi.org/10.1007/978-3-642-19309-5_41 · Full text. Journal: Computer Vision – ACCV ...
The proposed method involves using a pretrained transfer learning model framework to compute deep features from multitemporal remote sensing images.
Feb 6, 2024 · Unsupervised transfer learning offers a powerful framework for leveraging unlabeled data to extract informative representations that can be transferred across ...
Missing: Selective | Show results with:Selective
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Mar 15, 2021 · To train more robust anomaly detectors, we propose a new framework to perform unsupervised transfer learning (UTL) for one-class classification problems.
Missing: Selective Object
Jun 13, 2024 · We propose a transfer learning framework that is valid for a generic scenario. In this framework, generated images help to improve the performances of an ...
This paper explores a new research problem of un- supervised transfer learning across multiple spa- tiotemporal prediction tasks. Unlike most existing.