Virtually all computer vision machine learning models involve classification—for example, “how many humans are in the frame?” To train such a model, you need examples of every class of object the model is intended to detect. But what happens when a new type of object appears? Ask a model trained only on elephants and zebras to classify a giraffe and it will happily misclassify the image. Similarly, when you train a model to detect flaws (such as a broken bottle) on a production line, you need training data for every possible fault, which is impractical. The solution to this dilemma is FOMO-AD, an ML model for visual anomaly detection. FOMO-AD can be used along with classification models, in which case it flags instances where you shouldn’t trust the classifier (e.g., this animal is unlike anything I’ve ever seen), or it can be used standalone to detect anomalies without requiring the developer to define any explicit fault states.