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Found 3 papers, 1 papers with code
An NMF-Based Building Block for Interpretable Neural Networks With Continual Learning
Our approach aims to strike a better balance between these two aspects through the use of a building block based on NMF that incorporates supervised neural network training methods to achieve high predictive performance while retaining the desirable interpretability properties of NMF.
Parameter Reference Loss for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to utilize labeled data from a source domain to learn a model that generalizes to a target domain of unlabeled data.
Warping Peirce Quincuncial Panoramas
In this paper, we propose an algorithm and user-interface to mitigate these artifacts.
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