depood

Depood is a term used in data science to describe the process of depooding training data by identifying and removing data points that have been corrupted through poisoning or mislabeling. The aim is to improve the robustness and reliability of machine learning models by reducing the influence of adversarial or erroneous examples.

Origin and scope: The term is a portmanteau of “de-poison” and “data” and has appeared in academic discussions and industry blog posts since the late 2010s to denote methods for cleansing data in adversarial contexts. It is not a single standardized technique but a family of practices.

Techniques and workflow: Depood typically includes detecting anomalous instances through statistical or model-based indicators, measuring a point's influence on model loss or predictions, and removing or correcting suspect data points. Practices may involve data provenance, validation using held-out checks, reweighting or trimming outliers, and employing robust training objectives that lessen reliance on flagged examples.

Applications and limitations: Depood is used in security-conscious domains such as finance, healthcare, and critical infrastructure, as well as in general data cleansing pipelines. Limitations include the risk of discarding legitimate but rare data, potential bias from overly aggressive filtering, and increased computational overhead. Effective depood requires careful thresholding and ongoing evaluation.

See also: data poisoning; robust statistics; adversarial machine learning; data cleaning.