Semi-supervised time series classification
4 papers with code • 0 benchmarks • 1 datasets
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Most implemented papers
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification
Specifically, we propose time-series specific weak and strong augmentations and use their views to learn robust temporal relations in the proposed temporal contrasting module, besides learning discriminative representations by our proposed contextual contrasting module.
An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series Classification
Among the many time series learning tasks of great importance, we here focus on semi-supervised learning based on a graph representation of the data.
Semi-supervised Time Series Classification by Temporal Relation Prediction
Then, the temporal relation between those segments is predicted by SemiTime in a self-supervised manner.
SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence
In this work, edge users collaborate to train a Hyper-network in the server, generating personalized autoencoders for each user.