  This paper proposes a method for OOD detection. Questioning the premise of
previous studies that ID and OOD samples are separated distinctly, we consider
samples lying in the intermediate of the two and use them for training a
network. We generate such samples using multiple image transformations that
corrupt inputs in various ways and with different severity levels. We estimate
where the generated samples by a single image transformation lie between ID and
OOD using a network trained on clean ID samples. To be specific, we make the
network classify the generated samples and calculate their mean classification
accuracy, using which we create a soft target label for them. We train the same
network from scratch using the original ID samples and the generated samples
with the soft labels created for them. We detect OOD samples by thresholding
the entropy of the predicted softmax probability. The experimental results show
that our method outperforms the previous state-of-the-art in the standard
benchmark tests. We also analyze the effect of the number and particular
combinations of image corrupting transformations on the performance.
