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Found 4 papers, 1 papers with code
Benchmarking time series classification -- Functional data vs machine learning approaches
In order to assess the methods and implementations, we run a benchmark on a wide variety of representative (time series) data sets, with in-depth analysis of empirical results, and strive to provide a reference ranking for which method(s) to use for non-expert practitioners.
SELF: Learning to Filter Noisy Labels with Self-Ensembling
Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained with noisy labels for a long enough time.
Robust Learning Under Label Noise With Iterative Noise-Filtering
We consider the problem of training a model under the presence of label noise.
Robust Anomaly Detection in Images using Adversarial Autoencoders
Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis.
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