DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference in Image Recognition
This paper proposes a dual-supervised uncertainty inference (DS-UI) framework for improving Bayesian estimation-based UI in DNN-based image recognition. In the DS-UI, we combine the classifier of a DNN, i.e. , the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to...
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| Veröffentlicht in: | IEEE transactions on image processing Jg. 30; S. 9208 - 9219 |
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| Hauptverfasser: | , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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New York
IEEE
2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1057-7149, 1941-0042, 1941-0042 |
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| Abstract | This paper proposes a dual-supervised uncertainty inference (DS-UI) framework for improving Bayesian estimation-based UI in DNN-based image recognition. In the DS-UI, we combine the classifier of a DNN, i.e. , the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to obtain an MoGMM-FC layer. Unlike existing UI methods for DNNs, which only calculate the means or modes of the DNN outputs' distributions, the proposed MoGMM-FC layer acts as a probabilistic interpreter for the features that are inputs of the classifier to directly calculate the probabilities of them for the DS-UI. In addition, we propose a dual-supervised stochastic gradient-based variational Bayes (DS-SGVB) algorithm for the MoGMM-FC layer optimization. Unlike conventional SGVB and optimization algorithms in other UI methods, the DS-SGVB not only models the samples in the specific class for each Gaussian mixture model (GMM) in the MoGMM, but also considers the negative samples from other classes for the GMM to reduce the intra-class distances and enlarge the inter-class margins simultaneously for enhancing the learning ability of the MoGMM-FC layer in the DS-UI. Experimental results show the DS-UI outperforms the state-of-the-art UI methods in misclassification detection. We further evaluate the DS-UI in open-set out-of-domain/-distribution detection and find statistically significant improvements. Visualizations of the feature spaces demonstrate the superiority of the DS-UI. Codes are available at https://github.com/PRIS-CV/DS-UI . |
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| AbstractList | This paper proposes a dual-supervised uncertainty inference (DS-UI) framework for improving Bayesian estimation-based UI in DNN-based image recognition. In the DS-UI, we combine the classifier of a DNN, i.e. , the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to obtain an MoGMM-FC layer. Unlike existing UI methods for DNNs, which only calculate the means or modes of the DNN outputs' distributions, the proposed MoGMM-FC layer acts as a probabilistic interpreter for the features that are inputs of the classifier to directly calculate the probabilities of them for the DS-UI. In addition, we propose a dual-supervised stochastic gradient-based variational Bayes (DS-SGVB) algorithm for the MoGMM-FC layer optimization. Unlike conventional SGVB and optimization algorithms in other UI methods, the DS-SGVB not only models the samples in the specific class for each Gaussian mixture model (GMM) in the MoGMM, but also considers the negative samples from other classes for the GMM to reduce the intra-class distances and enlarge the inter-class margins simultaneously for enhancing the learning ability of the MoGMM-FC layer in the DS-UI. Experimental results show the DS-UI outperforms the state-of-the-art UI methods in misclassification detection. We further evaluate the DS-UI in open-set out-of-domain/-distribution detection and find statistically significant improvements. Visualizations of the feature spaces demonstrate the superiority of the DS-UI. Codes are available at https://github.com/PRIS-CV/DS-UI . This paper proposes a dual-supervised uncertainty inference (DS-UI) framework for improving Bayesian estimation-based UI in DNN-based image recognition. In the DS-UI, we combine the classifier of a DNN, i.e., the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to obtain an MoGMM-FC layer. Unlike existing UI methods for DNNs, which only calculate the means or modes of the DNN outputs' distributions, the proposed MoGMM-FC layer acts as a probabilistic interpreter for the features that are inputs of the classifier to directly calculate the probabilities of them for the DS-UI. In addition, we propose a dual-supervised stochastic gradient-based variational Bayes (DS-SGVB) algorithm for the MoGMM-FC layer optimization. Unlike conventional SGVB and optimization algorithms in other UI methods, the DS-SGVB not only models the samples in the specific class for each Gaussian mixture model (GMM) in the MoGMM, but also considers the negative samples from other classes for the GMM to reduce the intra-class distances and enlarge the inter-class margins simultaneously for enhancing the learning ability of the MoGMM-FC layer in the DS-UI. Experimental results show the DS-UI outperforms the state-of-the-art UI methods in misclassification detection. We further evaluate the DS-UI in open-set out-of-domain/-distribution detection and find statistically significant improvements. Visualizations of the feature spaces demonstrate the superiority of the DS-UI. Codes are available at https://github.com/PRIS-CV/DS-UI.This paper proposes a dual-supervised uncertainty inference (DS-UI) framework for improving Bayesian estimation-based UI in DNN-based image recognition. In the DS-UI, we combine the classifier of a DNN, i.e., the last fully-connected (FC) layer, with a mixture of Gaussian mixture models (MoGMM) to obtain an MoGMM-FC layer. Unlike existing UI methods for DNNs, which only calculate the means or modes of the DNN outputs' distributions, the proposed MoGMM-FC layer acts as a probabilistic interpreter for the features that are inputs of the classifier to directly calculate the probabilities of them for the DS-UI. In addition, we propose a dual-supervised stochastic gradient-based variational Bayes (DS-SGVB) algorithm for the MoGMM-FC layer optimization. Unlike conventional SGVB and optimization algorithms in other UI methods, the DS-SGVB not only models the samples in the specific class for each Gaussian mixture model (GMM) in the MoGMM, but also considers the negative samples from other classes for the GMM to reduce the intra-class distances and enlarge the inter-class margins simultaneously for enhancing the learning ability of the MoGMM-FC layer in the DS-UI. Experimental results show the DS-UI outperforms the state-of-the-art UI methods in misclassification detection. We further evaluate the DS-UI in open-set out-of-domain/-distribution detection and find statistically significant improvements. Visualizations of the feature spaces demonstrate the superiority of the DS-UI. Codes are available at https://github.com/PRIS-CV/DS-UI. |
| Author | Xue, Jing-Hao Ma, Zhanyu Xie, Jiyang Zhang, Guoqiang Zheng, Yinhe Guo, Jun Sun, Jian |
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| References | van amersfoort (ref29) 2020 ref14 krizhevsky (ref43) 2009 simonyan (ref41) 2014 hendrycks (ref11) 2017 ref17 ref16 ref18 goodfellow (ref50) 2015 ref46 malinin (ref9) 2018 ref45 ref47 ref42 hoffman (ref36) 2013; 14 fan (ref35) 2015 kingma (ref48) 2015 malinin (ref13) 2019 ref7 ref4 ref3 ref6 altosaar (ref34) 2018 ref5 mo?ejko (ref28) 2018 piergiovanni (ref31) 2019 ref37 li (ref23) 2016 wang (ref33) 2018 ref32 netzer (ref44) 2011 ref2 teye (ref40) 2018 kong (ref15) 2020 ref1 hinton (ref22) 2012 maddox (ref12) 2019 maas (ref49) 2013; 30 bishop (ref39) 2006 lee (ref24) 2018 blundell (ref21) 2015 gal (ref10) 2016 lakshminarayanan (ref8) 2017 ranganath (ref38) 2014 ref25 ma (ref19) 2011; 33 havasi (ref30) 2021 ma (ref20) 2020; 31 antorán (ref26) 2021 guo (ref27) 2017 van d maaten (ref51) 2008; 9 |
| References_xml | – start-page: 1387 year: 2015 ident: ref35 article-title: Fast second-order stochastic backpropagation for variational inference publication-title: Proc Adv Neural Inf Process Syst – start-page: 1 year: 2014 ident: ref41 article-title: Very deep convolutional networks for large-scale image recognition publication-title: Proc IEEE/CVF Conf Comput Vis Pattern Recognit (CVPR) – ident: ref47 doi: 10.1109/CVPR.2019.00241 – start-page: 7047 year: 2018 ident: ref9 article-title: Predictive uncertainty estimation via prior networks publication-title: Proc Adv Neural Inf Process Syst – start-page: 6402 year: 2017 ident: ref8 article-title: Simple and scalable predictive uncertainty estimation using deep ensembles publication-title: Proc Adv Neural Inf Process Syst (NIPS) – volume: 30 start-page: 3 year: 2013 ident: ref49 article-title: Rectifier nonlinearities improve neural network acoustic models publication-title: Proc Int Conf Mach Learn (ICML) – start-page: 1 year: 2021 ident: ref26 article-title: Getting a CLUE: A method for explaining uncertainty estimates publication-title: Proc Int Conf Learn Represent – ident: ref42 doi: 10.1109/CVPR.2016.90 – volume: 14 start-page: 1303 year: 2013 ident: ref36 article-title: Stochastic variational inference publication-title: J Mach Learn Res – start-page: 4907 year: 2018 ident: ref40 article-title: Bayesian uncertainty estimation for batch normalized deep networks publication-title: Proc Int Conf Mach Learn (ICML) – ident: ref17 doi: 10.1080/10618600.2016.1200472 – ident: ref6 doi: 10.1109/TIP.2020.2970248 – volume: 33 start-page: 2160 year: 2011 ident: ref19 article-title: Bayesian estimation of beta mixture models with variational inference publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2011.63 – year: 2009 ident: ref43 article-title: Learning multiple layers of features from tiny images – ident: ref32 doi: 10.1109/ICASSP.2015.7178776 – ident: ref37 doi: 10.1109/CVPR.2018.00169 – start-page: 1613 year: 2015 ident: ref21 article-title: Weight uncertainty in neural networks publication-title: Proc Int Conf Mach Learn (ICML) – year: 2018 ident: ref28 article-title: Inhibited softmax for uncertainty estimation in neural networks publication-title: arXiv 1810 01861 – ident: ref45 doi: 10.1007/s11263-015-0816-y – ident: ref16 doi: 10.1109/CVPR42600.2020.01183 – ident: ref2 doi: 10.1109/CVPR42600.2020.00977 – start-page: 1 year: 2015 ident: ref50 article-title: Explaining and harnessing adversarial examples publication-title: Proc Int Conf Learn Represent – start-page: 1050 year: 2016 ident: ref10 article-title: Dropout as a Bayesian approximation: Representing model uncertainty in deep learning publication-title: Proc Int Conf Mach Learn (ICML) – ident: ref1 doi: 10.1109/CVPR42600.2020.01106 – start-page: 1961 year: 2018 ident: ref34 article-title: Proximity variational inference publication-title: Proc Int Conf Artif Intell Statist – start-page: 814 year: 2014 ident: ref38 article-title: Black box variational inference publication-title: Proc Int Conf Artif Intell Statist – ident: ref46 doi: 10.5244/C.31.42 – start-page: 1 year: 2015 ident: ref48 article-title: Adam: A method for stochastic optimization publication-title: Proc Int Conf Learn Represent (ICLR) – start-page: 5152 year: 2019 ident: ref31 article-title: Temporal Gaussian mixture layer for videos publication-title: Proc Int Conf Mach Learn (ICML) – year: 2012 ident: ref22 article-title: Improving neural networks by preventing co-adaptation of feature detectors publication-title: arXiv 1207 0580 – ident: ref25 doi: 10.1109/TNNLS.2020.2980004 – ident: ref3 doi: 10.1109/TIP.2020.2973812 – start-page: 1 year: 2018 ident: ref24 article-title: A simple unified framework for detecting out-of-distribution samples and adversarial attacks publication-title: Proc Adv Neural Inf Process Syst – start-page: 1 year: 2011 ident: ref44 article-title: Reading digits in natural images with unsupervised feature learning publication-title: Proc NIPS Workshop Deep Learn Unsupervised Feature Learn – start-page: 1 year: 2020 ident: ref15 article-title: SDE-Net: Equipping deep neural networks with uncertainty estimates publication-title: Proc Int Conf Mach Learn (ICML) – ident: ref18 doi: 10.1016/j.csda.2006.01.001 – ident: ref14 doi: 10.1109/CVPR42600.2020.00575 – start-page: 1788 year: 2016 ident: ref23 article-title: Preconditioned stochastic gradient Langevin dynamics for deep neural networks publication-title: Proc AAAI Conf Artif Intell – start-page: 1 year: 2021 ident: ref30 article-title: Training independent subnetworks for robust prediction publication-title: Proc Int Conf Learn Represent – ident: ref5 doi: 10.1109/CVPR46437.2021.01131 – ident: ref7 doi: 10.1109/TPAMI.2019.2956930 – volume: 9 start-page: 2579 year: 2008 ident: ref51 article-title: Visualizing data using t-SNE publication-title: J Mach Learn Res – volume: 31 start-page: 2240 year: 2020 ident: ref20 article-title: Insights into multiple/single lower bound approximation for extended variational inference in non-Gaussian structured data modeling publication-title: IEEE Trans Neural Netw Learn Syst – start-page: 14520 year: 2019 ident: ref13 article-title: Reverse KL-divergence training of prior networks: Improved uncertainty and adversarial robustness publication-title: Proc Adv Neural Inf Process Syst – start-page: 13153 year: 2019 ident: ref12 article-title: A simple baseline for Bayesian uncertainty in deep learning publication-title: Proc Adv Neural Inf Process Syst – year: 2006 ident: ref39 publication-title: Pattern Recognition and Machine Learning – start-page: 1 year: 2017 ident: ref11 article-title: A baseline for detecting misclassified and out-of-distribution examples in neural networks publication-title: Proc Int Conf Learn Represent (ICLR) – ident: ref4 doi: 10.1109/TIP.2021.3055617 – start-page: 1321 year: 2017 ident: ref27 article-title: On calibration of modern neural networks publication-title: Proc Int Conf Mach Learn (ICML) – start-page: 249 year: 2018 ident: ref33 article-title: An unsupervised deep learning framework via integrated optimization of representation learning and GMM-based modeling publication-title: Proc Asian Conf Comput Vis – start-page: 9690 year: 2020 ident: ref29 article-title: Uncertainty estimation using a single deep deterministic neural network publication-title: Proc Int Conf Mach Learn |
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| SubjectTerms | Algorithms Bayes methods Classifiers Deep learning dual supervised framework Feature extraction Image recognition Inference mixture of gaussian mixture models Object recognition Optimization Probabilistic logic Probabilistic models Statistical methods Stochastic processes Uncertainty uncertainty inference |
| Title | DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference in Image Recognition |
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