The idea is to first develop a prediction model without concern for the downstream risk profile or robustness guarantee, and then utilize calibration (or recalibration) methods to quantify the uncertainty of the prediction.

Fine-grained zero-shot learning task requires some form of side-information to transfer discriminative information from seen to unseen classes.

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