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This paper proposes a method for OOD detection. Questioning the premise ofprevious studies that ID and OOD samples are separated distinctly, we considersamples lying in the intermediate of the two and use them for training anetwork. We generate such samples using multiple image transformations thatcorrupt inputs in various ways and with different severity levels. We estimatewhere the generated samples by a single image transformation lie between ID andOOD using a network trained on clean ID samples. To be specific, we make thenetwork classify the generated samples and calculate their mean classificationaccuracy, using which we create a soft target label for them. We train the samenetwork from scratch using the original ID samples and the generated sampleswith the soft labels created for them. We detect OOD samples by thresholdingthe entropy of the predicted softmax probability. The experimental results showthat our method outperforms the previous state-of-the-art in the standardbenchmark tests. We also analyze the effect of the number and particularcombinations of image corrupting transformations on the performance.

Bridging In- and Out-of-distribution Samples for Their Better Discriminability