We lifted 3 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 |
|---|---|---|
| imperial-qore/tranad | canonical | 0 of 2 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| normalize | Ran | this paper's copy was not recorded; identical code first harvested from falihgoz/cgad pointer only · get_code("7baf753df5b73419") |
| load_and_save | Not yet run | imperial-qore/tranad/preprocess.py code served (permissive licence) · get_code("7245a581e4d8d272") |
| normalize2 | Not yet run | imperial-qore/tranad/preprocess.py code served (permissive licence) · get_code("8521252d50d05f1b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications. However, building a system that is able to quickly and accurately pinpoint anomalous observations is a challenging problem. This is due to the lack of anomaly labels, high data volatility and the demands of ultra-low inference times in modern applications. Despite the recent developments of deep learning approaches for anomaly detection, only a few of them can address all of these challenges. In this paper, we propose TranAD, a deep transformer network based anomaly detection and diagnosis model which uses attention-based sequence encoders to swiftly perform inference with the knowledge of the broader temporal trends in the data. TranAD uses focus score-based self-conditioning to enable robust multi-modal feature extraction and adversarial training to gain stability. Additionally, model-agnostic meta learning (MAML) allows us to train the model using limited data. Extensive empirical studies on six publicly available datasets demonstrate that TranAD can outperform state-of-the-art baseline methods in detection and diagnosis performance with data and time-efficient training. Specifically, TranAD increases F1 scores by up to 17%, reducing training times by up to 99% compared to the baselines.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2201.07284")
get_code_for_paper("2201.07284")
have("2201.07284")
Connect an agent — have() is free.