Stephan Mandt, Marius Kloft, Padhraic Smyth, Chen Qiu, Maja Rudolph, Aodong Li
We lifted 1 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| aodongli/Active-SOEL | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| kmeans_diverse | Ran | aodongli/Active-SOEL/NTL/models/Query_strategies.py code served (permissive licence) · get_code("b31c4a377b589a88") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Selecting informative data points for expert feedback can significantly improve the performance of anomaly detection (AD) in various contexts, such as medical diagnostics or fraud detection. In this paper, we determine a set of theoretical conditions under which anomaly scores generalize from labeled queries to unlabeled data. Motivated by these results, we propose a data labeling strategy with optimal data coverage under labeling budget constraints. In addition, we propose a new learning framework for semi-supervised AD. Extensive experiments on image, tabular, and video data sets show that our approach results in stateof-the-art semi-supervised AD performance under labeling budget constraints.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2302.07832")
get_code_for_paper("2302.07832")
have("2302.07832")
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