Graph imputation neural networks augment semi-supervised datasets up to 10x by reconstructing heavily damaged samples on a similarity graph, improving over fully-supervised baselines on benchmarks.
arXiv preprint arXiv:1905.02249 (2019)
2 Pith papers cite this work. Polarity classification is still indexing.
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An ensemble of CRNNs trained with consistency regularization and MixUp on mixed labeled/unlabeled data reaches 42.0% event-based F-measure on DCASE 2019 Task 4, beating the 25.8% baseline.
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Efficient data augmentation using graph imputation neural networks
Graph imputation neural networks augment semi-supervised datasets up to 10x by reconstructing heavily damaged samples on a similarity graph, improving over fully-supervised baselines on benchmarks.
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HODGEPODGE: Sound event detection based on ensemble of semi-supervised learning methods
An ensemble of CRNNs trained with consistency regularization and MixUp on mixed labeled/unlabeled data reaches 42.0% event-based F-measure on DCASE 2019 Task 4, beating the 25.8% baseline.