Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Paper summary Liang et al. propose a perturbation-based approach for detecting out-of-distribution examples using a network’s confidence predictions. In particular, the approaches based on the observation that neural network’s make more confident predictions on images from the original data distribution, in-distribution examples, than on examples taken from a different distribution (i.e., a different dataset), out-distribution examples. This effect can further be amplified by using a temperature-scaled softmax, i.e., $ S_i(x, T) = \frac{\exp(f_i(x)/T)}{\sum_{j = 1}^N \exp(f_j(x)/T)}$ where $f_i(x)$ are the predicted logits and $T$ a temperature parameter. Based on these softmax scores, perturbations $\tilde{x}$ are computed using $\tilde{x} = x - \epsilon \text{sign}(-\nabla_x \log S_{\hat{y}}(x;T))$ where $\hat{y}$ is the predicted label of $x$. This is similar to “one-step” adversarial examples; however, in contrast of minimizing the confidence of the true label, the confidence in the predicted label is maximized. This, applied to in-distribution and out-distribution examples is illustrated in Figure 1 and meant to emphasize the difference in confidence. Afterwards, in- and out-distribution examples can be distinguished using simple thresholding on the predicted confidence, as shown in various experiment, e.g., on Cifar10 and Cifar100. https://i.imgur.com/OjDVZ0B.png Figure 1: Illustration of the proposed perturbation to amplify the difference in confidence between in- and out-distribution examples. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).
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Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Shiyu Liang and Yixuan Li and R. Srikant
arXiv e-Print archive - 2017 via Local arXiv
Keywords: cs.LG, stat.ML

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Summary by David Stutz 3 months ago
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