%0 Journal Article %1 guo2017calibration %A Guo, Chuan %A Pleiss, Geoff %A Sun, Yu %A Weinberger, Kilian Q. %D 2017 %K calibration deep-learning %T On Calibration of Modern Neural Networks %U http://arxiv.org/abs/1706.04599 %X Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.

@article{guo2017calibration, abstract = {Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-of-the-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -- a single-parameter variant of Platt Scaling -- is surprisingly effective at calibrating predictions.}, added-at = {2019-03-12T00:04:42.000+0100}, author = {Guo, Chuan and Pleiss, Geoff and Sun, Yu and Weinberger, Kilian Q.}, biburl = {https://www.bibsonomy.org/bibtex/2ce2c6f8772a74f4a19234111b95e75da/kirk86}, description = {[1706.04599] On Calibration of Modern Neural Networks}, interhash = {315c62001aeac75c9ddacc6a8afa7f8e}, intrahash = {ce2c6f8772a74f4a19234111b95e75da}, keywords = {calibration deep-learning}, note = {cite arxiv:1706.04599Comment: ICML 2017}, timestamp = {2019-03-12T00:04:42.000+0100}, title = {On Calibration of Modern Neural Networks}, url = {http://arxiv.org/abs/1706.04599}, year = 2017 }