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Found 3 papers, 2 papers with code
Robust Learning Under Label Noise With Iterative Noise-Filtering
We consider the problem of training a model under the presence of label noise.
Consistency-based anomaly detection with adaptive multiple-hypotheses predictions
Thus, due to the lack of representative data, the wide-spread discriminative approaches cannot cover such learning tasks, and rather generative models, which attempt to learn the input density of the normal cases, are used.
Anomaly Detection With Multiple-Hypotheses Predictions
In one-class-learning tasks, only the normal case (foreground) can be modeled with data, whereas the variation of all possible anomalies is too erratic to be described by samples.
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