{"url":"/dataset/anoshift","name":"AnoShift","full_name":"AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection","description_markdown":"AnoShift is a large-scale anomaly detection benchmark, which focuses on splitting the test data based on its temporal distance to the training set, introducing three testing splits: IID, NEAR, and FAR. This testing scenario proves to capture the in-time performance degradation of anomaly detection methods for classical to masked language models. \r\n\r\nAnoShift benchmark aims to enable a better estimate of the anomaly detection model’s performance, under natural distribution shifts that occur over time in the input, closer to the real-world performance, leading to more robust anomaly detection algorithms.\r\n\r\nThe benchmark is based on the Kyoto-2016 dataset (https://www.takakura.com/Kyoto_data/).","description_withheld":null,"homepage":"https://github.com/bit-ml/AnoShift","introduced_date":"2022-06-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/anoshift-a-distribution-shift-benchmark-for","title":"AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection","first_author":"Marius Dragoi","url":null},"license":{"name":"BSD-3","url":"https://github.com/bit-ml/AnoShift/blob/main/LICENSE"},"modalities":[{"name":"Tabular","url":"/datasets/modality/tabular"},{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Unsupervised Anomaly Detection","url":"/task/unsupervised-anomaly-detection","datasets_with_task":"/datasets/task/unsupervised-anomaly-detection"}],"languages":[],"variants":["AnoShift"],"data_loaders":[{"repo":"https://github.com/bit-ml/anoshift","url":"https://github.com/bit-ml/anoshift","frameworks":["pytorch"]}],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/unsupervised-anomaly-detection-on-anoshift","task":"Unsupervised Anomaly Detection","dataset_variant":"AnoShift","rows":15,"metrics":["ROC-AUC FAR","ROC-AUC IID","ROC-AUC NEAR","ROC-AUC-ID (In-Distribution setup)"],"first_row_in_archive_order":{"model":"ACR-NTL (zero-shot, test anomaly ratio=1%)","paper":"/paper/zero-shot-anomaly-detection-via-batch-1","metrics":{"ROC-AUC FAR":"62.5"},"code_links":[{"title":"aodongli/zero-shot-ad-via-batch-norm","url":"https://github.com/aodongli/zero-shot-ad-via-batch-norm"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/zero-shot-anomaly-detection-via-batch-1","title":"Zero-Shot Anomaly Detection via Batch Normalization","date":"2023-02-15","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/anoshift-a-distribution-shift-benchmark-for","title":"AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection","date":"2022-06-30","rows_on_this_dataset":11,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":0,"samples_unverified":13,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":14,"samples_ran":1,"samples_unverified":13,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}