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Found 3 papers, 1 papers with code
Weakly Supervised Temporal Anomaly Segmentation with Dynamic Time Warping
Most recent studies on detecting and localizing temporal anomalies have mainly employed deep neural networks to learn the normal patterns of temporal data in an unsupervised manner.
Bootstrapping User and Item Representations for One-Class Collaborative Filtering
To make the representations of positively-related users and items similar to each other while avoiding a collapsed solution, BUIR adopts two distinct encoder networks that learn from each other; the first encoder is trained to predict the output of the second encoder as its target, while the second encoder provides the consistent targets by slowly approximating the first encoder.
One-class Classification Robust to Geometric Transformation
Recent studies on one-class classification have achieved a remarkable performance, by employing the self-supervised classifier that predicts the geometric transformation applied to in-class images.
