  Out-of-distribution (OOD) detection is essential in identifying test samples
that deviate from the in-distribution (ID) data upon which a standard network
is trained, ensuring network robustness and reliability. This paper introduces
OOD knowledge distillation, a pioneering learning framework applicable whether
or not training ID data is available, given a standard network. This framework
harnesses OOD-sensitive knowledge from the standard network to craft a binary
classifier adept at distinguishing between ID and OOD samples. To accomplish
this, we introduce Confidence Amendment (CA), an innovative methodology that
transforms an OOD sample into an ID one while progressively amending prediction
confidence derived from the standard network. This approach enables the
simultaneous synthesis of both ID and OOD samples, each accompanied by an
adjusted prediction confidence, thereby facilitating the training of a binary
classifier sensitive to OOD. Theoretical analysis provides bounds on the
generalization error of the binary classifier, demonstrating the pivotal role
of confidence amendment in enhancing OOD sensitivity. Extensive experiments
spanning various datasets and network architectures confirm the efficacy of the
proposed method in detecting OOD samples.
