![]() ![]() Secondly, due to the data complexity, it is challenging to differentiate the incorrect predictions from the correct ones on real-world large-scale datasets. ![]() Firstly, correct predictions are generally dominant over incorrect predictions. In such a setting, we observe that the trustworthiness predictors trained with prior-art loss functions, i.e., the cross entropy loss, focal loss, and true class probability confidence loss, are prone to view both correct predictions and incorrect predictions to be trustworthy. ![]() In this work, we study the problem of predicting trustworthiness on real-world large-scale datasets, where the task is more challenging due to high-dimensional features, diverse visual concepts, and a large number of samples. Prior efforts have been proven to be reliable on small-scale datasets. Abstract: Understanding the trustworthiness of a prediction yielded by a classifier is critical for the safe and effective use of AI models. ![]()
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