Title: Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks,
Abstract: We consider the problem of detecting out-of-distribution images in neural
networks. We propose ODIN, a simple and effective method that does not require
any change to a pre-trained neural network. Our method is based on the
observation that using temperature scaling and adding small perturbations to
the input can separate the softmax score distributions between in- and
out-of-distribution images, allowing for more effective detection. We show in a
series of experiments that ODIN is compatible with diverse network
architectures and datasets. It consistently outperforms the baseline approach
by a large margin, establishing a new state-of-the-art performance on this
task. For example, ODIN reduces the false positive rate from the baseline 34.7%
to 4.3% on the DenseNet (applied to CIFAR-10) when the true positive rate is
95%.