A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks.¶
Hendrycks, D., & Gimpel, K. (2017). A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks. International Conference on Learning Representations (ICLR).
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Primes¶
- Extrapolation Beyond Sampled Regime
- Machine learning: a classifier trained on one input distribution emits high softmax confidence on out-of-distribution inputs; out-of-distribution detection exists as a field precisely because the base model's confidence apparatus is self-blind.
This sourceEstablishes out-of-distribution detection as a task precisely because a base model's softmax confidence does not register that an input lies outside the training distribution.
- Machine learning: a classifier trained on one input distribution emits high softmax confidence on out-of-distribution inputs; out-of-distribution detection exists as a field precisely because the base model's confidence apparatus is self-blind.
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