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Found 3 papers, 1 papers with code
Stylist: Style-Driven Feature Ranking for Robust Novelty Detection
Similar to works in out-of-distribution generalization, we propose to use the formalization of separating into semantic or content changes, that are relevant to our task, and style changes, that are irrelevant.
Environment-biased Feature Ranking for Novelty Detection Robustness
Thus, we propose a method that starts with a pretrained embedding and a multi-env setup and manages to rank the features based on their environment-focus.
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!
We introduce a formalization and benchmark for the unsupervised anomaly detection task in the distribution-shift scenario.
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