2023
DOI: 10.1109/access.2023.3280824
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MESAC: Learning to Remove Mismatches via Maximizing the Expected Score of Sample Consensuses

Abstract: Most learning-based methods require labelling the training data, which is time-consuming and gives rise to wrong labels. To address the labelling issues thoroughly, we propose an unsupervised learning framework to remove mismatches by maximizing the expected score of sample consensuses (MESAC). The proposed MESAC can train various permutation invariant networks (PINs) based on training data with no labels, and has three distinct merits: 1) the framework can train various PINs in an unsupervised mode such that … Show more

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