Training Confidence Calibrated Classifiers For Detecting Out Of Distribution Samples
By design discriminatively trained neural network classifiers produce reliable predictions only for in distribution samples. Let an input x xand a label y y 1 k.
Training Confidence Calibrated Classifiers For Detecting Out Of
Training confidence calibrated classifiers for detecting out of distribution samples.
Training confidence calibrated classifiers for detecting out of distribution samples. The first one forces samples from out of distribution less confident by the classifier and the second one is for implicitly generating most effective training samples for the first one. The problem of detecting whether a test sample is from in distribution i e training distribution by a classifier or out of distribution sufficiently different from it arises in many real world machine learning applications. Python3 main py h.
Detect whether a test sample is from in distribution i e training distribution by a classifier formally it can be formulated as a binary classification problem. However the state of art deep neural networks are known to be highly overconfident in their predictions i e do not distinguish in and out of distributions. For their real world deployments detecting out of distribution ood samples is essential.
Training confidence calibrated classifier for detecting out of distribution samples. The confidence loss requires training samples from out of distribution which are often hard to sample. The problem of detecting whether a test sample is from in distribution i e training distribution by a classifier or out of distribution sufficiently different from it arises in many real world machine learning applications.
This project is for the paper training confidence calibrated classifier for detecting out of distribution samples. The first one forces samples from out of distribution less confident by the classifier and the second one is for implicitly generating most effective training samples for the first one. In essence our method jointly trains both classification and generative neural networks for out of distribution.
Published as a conference paper at iclr 2018 training confidence calibrated classifiers for detecting out of distribution samples kimin lee honglak leex. The overconfidence issue of dnns is highly related to the problem of detecting out of distribution. Ykibok lee jinwoo shin korea advanced institute of science and technology daejeon korea yuniversity of michigan ann arbor mi 48109 xgoogle brain mountain view ca 94043 abstract the problem of detecting whether a test sample is from in.
Some codes are from odin pytorch. That is the detection accuracy for the cross entropy loss red bar when cifar 10 is used as the in distribution data and svhn as the out of distribution data. To handle this issue we propose a joint training scheme for the confident classifier and the proposed gan which generates boundary samples in the low density area of in distribution i e close to out of distribution.
Various hyperparameters can be set prior to training to see which run.
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