A regularization criterion that, differently from [dropout](https://paperswithcode.com/method/dropout) and its variants, is deterministic rather than random. It grounds on the empirical evidence that feature descriptors with larger L2-norm and highly-active nodes are strongly correlated to confident class predictions. Thus, the criterion guides towards dropping a percentage of the most active nodes of the descriptors, proportionally to the estimated class probability

**TABBIE** is a pretraining objective (*corrupt cell detection*) that learns exclusively from tabular data. Unlike other approaches, TABBIE provides embeddings of all table substructures (cells, rows, and columns). TABBIE can be seen as a table embedding model trained to detect corrupted cells, inspired by the [ELECTRA](https://www.paperswithcode.com/method/electra) objective function.

TABBIE

TABBIE: Pretrained Representations of Tabular Data

Attack on image classifiers by a image solarization through greedy random search.

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com