person Author: Process Fellows
Corner cases are extreme or rare scenarios that lie within the expected value range. They occur rarely, but are still part of the domain. Examples:

• An image recognition model for cats recognizes a cat with a very unusual coat color (e.g. completely pink).
• An autonomous vehicle encounters an unusual traffic route, but this is provided for in the road traffic regulations (e.g. an extremely tight hairpin bend).
• A very high or very low value in a time series data set (e.g. a share price rises by 100% in a single day).

Corner cases often require fine-tuning or special training data to ensure that the model works reliably even in these rare cases.

Unexpected cases are scenarios that the model did not anticipate because they are outside the training domain or are not covered by the data model.
Examples:

• An image recognition model for cats is supposed to recognize a cat, but the image contains a new animal species that the model has never seen (e.g. a Sphynx cat if the model only knows furry cats).
• An autonomous vehicle encounters a completely new traffic regulation or an unexpected road situation that it has never seen before in training data (e.g. a sudden earthquake and collapsing bridges).
• A language model is confronted with a new language or dialect that it does not know.

Unexpected cases often cannot be solved simply by using more training data. This is where approaches such as out-of-distribution (OOD) detection, anomaly detection or unsupervised learning help to react to completely new cases.