Igeood: An Information Geometry Approach to Out-of-Distribution Detection

1 INTRODUCTION . Deep neural networks ( DNNs ) achieve state-of-the-art in several classification tasks as they are known to generalize well on data with similar distribution to the training set . Whereas , in many practical applications , the training set does not reflect well enough the real-life environment ( QuioneroCandela et al. , 2009 ) which is often non-stationary and sometimes with unpredictable events . As a consequence , matching the training scenario to reality can be either not possible or too complex . The failure of machine learning ( ML ) models to adapt to non-stationary distributions could limit their adoption in critical systems ( e.g. , autonomous devices , healthcare applications ) . Out-of-Distribution ( OOD ) or novelty detection is one of the main objectives in conceiving reliable ML systems ( Amodei et al. , 2016 ) . A typical application is in monitoring ML-based online services for periodically shifting distributions . However , tracking changes in the underlying data distribution is challenging as they contain unusual ( irregular or unexpected ) events and have large dimensions . For instance , relying on the intrinsic properties of the ML models and their statistical behavior in the presence of in-distribution data is key to identifying OOD samples . Classic approaches to OOD detection consist of deriving metrics for detecting those abnormalities from the lens of ML models ( e.g. , softmax output , latent representations across layers ) , provided that often only a single test example is available . Besides , those metrics are subject to potential limitations inherent to practical scenarios depending on the level of information access in the ML model , e.g. , only having access to the last layer or all intermediate layers . The baseline approach for OOD detection relies on the predictive uncertainty of DNNs . Hendrycks & Gimpel ( 2017 ) demonstrated that OOD samples , in general , induce DNN classifiers to output less confident softmax scores . Whereas existing state-of-the-art methods on classification problems still output high accuracy even under dataset shift . For instance , Ovadia et al . ( 2019 ) show that as the accuracy of the underlying DNN increases , the supervisors ’ outlier detection accuracy also improves . Unfortunately , also the variance increases . Henriksson et al . ( 2021 ) observed that small changes in model parameters that marginally impact the accuracy could have a degrading impact on the performance of the OOD discriminator . This challenge is not exclusive to discriminative models . Deep generative models also fall short in discerning OOD from in-distribution samples . Nalisnick et al . ( 2019 ) raise awareness of the fact that deep generative models also may output a higher likelihood to OOD samples . They show that , even though the samples from the in-distribution CIFAR-10 ( Krizhevsky et al. , 2009 ) dataset ( e.g. , cats , dogs , airplanes , ships ) are conceptually and visually different from house numbers from SVHN ( Netzer et al. , 2011 ) dataset , DNN-based classifiers may still assign a high likelihood to SVHN samples . In this paper , we propose IGEOOD , a new unified and effective method to perform OOD detection by rigorously exploring the information-geometric properties of the feature space on various depths of a DNN . IGEOOD provides a flexible framework that applies to any pre-trained softmax neural classifier . A key ingredient of IGEOOD is the Fisher-Rao distance . This distance is used as an effective differential geometry tool for clustering and as a distance in the context of multivariate Gaussian pdfs ( Pinele et al. , 2020 ; Strapasson et al. , 2016 ) . In our context , we measure the dissimilarity between probability ( in and out ) distributions , as the length of the shortest path within the manifold induced by the underlying class of distributions ( i.e. , the softmax probabilities of the neural classifier or the densities modeling the learned representations across the layers ) . By doing so , we can explore statistical invariances of the geometric properties of the learned features ( Bronstein et al. , 2021 ) . Our method adapts to the various scenarios depending on the level of information access of the DNN and uses only in-distribution samples but can also benefit ( if available ) from OOD samples . 1.1 CONTRIBUTIONS . Our work studies the problem of OOD detection and advances state-of-the-art in very different ways . • To the best of our knowledge , this is the first work studying information geometry tools to devise a unified metric for OOD detection . We derive an explicit characterization of the Fisher-Rao distance based on the information-geometric properties of the softmax probabilities of the neural classifier and the class of multivariate Gaussian pdfs . In general terms , our Fisher-Rao-based metric measures the mismatch–in the geometry space–between probability density functions of the pre-trained DNN classifier conditioning on test and indistribution samples . Section 3 shows in detail how IGEOOD is used for OOD detection . • Experiments on BLACK-BOX and GREY-BOX setups using various datasets , architectures , and classification tasks show that IGEOOD is competitive with state-of-the-art methods . In the BLACK-BOX setup , we assume that only the outputs , i.e. , the logits of the DNN , are available . In the GREY-BOX setup , we allow access to all parameters of the network ; however , the detection must be performed using only the output softmax probabilities . The latter permits input pre-processing which introduces a small ( additive ) noise according to the direction of the gradients w.r.t the test sample . This pre-processing allows for further discrimination between in- and out-of-distribution samples . Our benchmark contains two DNN architectures , three in-distribution datasets , and nine OOD datasets . • In a WHITE-BOX setting , we combine the logits with the low-level features of the DNN to leverage further useful statistical information of the encoded in-distribution data . We model the pre-trained latent representations as a mixture of Gaussian pdfs with a diagonal covariance matrix . Under this assumption , we derive a confidence score based on the Fisher-Rao distance between conditional pdfs corresponding to the test and the closest indistribution samples . Experiments based on various datasets , architectures , and classification tasks clearly show consistent improvement of IGEOOD , achieving new state-of-the-art performance on a couple of benchmarks . In particular , we increased the average TNR at 95 % TPR by 11.2 % with tuning on OOD data and by 2.5 % with tuning on adversarial data compared to Lee et al . ( 2018 ) . 1.2 RELATED WORKS . OOD discriminators consist of a binary classifier to distinguish between in- and out-of-distribution samples . A few works ( Shalev et al. , 2018 ; Hendrycks et al. , 2019 ; Bitterwolf et al. , 2020 ; Mohseni et al. , 2020 ; Winkens et al. , 2020 ; Vyas et al. , 2018 ; Hein et al. , 2019 ) propose retraining the base ( or an auxiliary ) model with synthetic or ground truth OOD samples to serve as a classifier and as an OOD discriminator . Disposing of both OOD and in-distribution samples during training enables the latent representations to learn the decision boundaries to facilitate OOD detection . These methods will not be compared to ours in this work , as they entail retraining or modifying the base neural network by using OOD data to further train parameters . Nagarajan et al . ( 2021 ) studies failure modes of OOD detection methods to better understand how to improve them , especially how spurious features like the background can vastly degrade detection performance . Lee et al . ( 2021 ) leverage OOD data as a regularization technique to improve the generalization and robustness of current neural networks . References ( Schlegl et al. , 2017 ; Kirichenko et al. , 2020 ; Choi & Jang , 2019 ; Vernekar et al. , 2019 ; Xiao et al. , 2020 ; Ren et al. , 2019 ; Zhang et al. , 2021 ; Mahmood et al. , 2021 ; Zhang et al. , 2020 ; Zisselman & Tamar , 2020 ) study OOD detection in the context of generative models for density estimation . Open set recognition ( Bendale & Boult , 2016 ) , outlier or anomaly detection ( Pimentel et al. , 2014 ) , concept drift detection ( Quionero-Candela et al. , 2009 ) , and adversarial attacks detection ( Goodfellow et al. , 2015 ; Madry et al. , 2018 ) are related topics . BLACK-BOX and GREY-BOX scenarios . It is often the case on ML as a service ( Ribeiro et al. , 2015 ) that the model ’ s parameters knowledge and access are restricted to the end-user , granting access only to the computation of the forward and the logits or softmax outputs . The baseline work ( Hendrycks & Gimpel , 2017 ) for BLACK-BOX techniques considers simply the unscaled maximum value of the softmax ( MSP ) as OOD score . In some cases , this confidence score is enough to distinguish between in-distribution and out-of-distribution examples , but it also may assign overconfident values to OOD examples ( Hein et al. , 2019 ) . ODIN ’ s ( Liang et al. , 2018 ) method has two variations . The BLACK-BOX variation consists of temperature scaling the softmax outputs . While the GREYBOX variation also uses an input pre-processing technique that calculates the gradient of the model parameters and adds to the input in an adversarial manner for a more effective OOD detection . Hsu et al . ( 2020 ) proposes a variation of ODIN that does not need access to OOD data for validation . Liu et al . ( 2020 ) proposes an energy-based OOD score . Without retraining , they substitute the softmax confidence score by the free energy function with a temperature parameter . They also propose a GREY-BOX variation with posterior processing for improved results . Fine-tuning is done differently across the literature and should be considered when comparing methodologies . WHITE-BOX scenario . This class of OOD detectors has access to all intermediate layer outputs . Naturally , discriminators have access to more information than the BLACK-BOX or GREY-BOX setups , warranting greater detection capacity . Batch-normalization statistics between layers are used ( Quintanilha et al. , 2019 ) to fit a logistic regression that serves as an OOD detection score . Sastry & Oore ( 2020 ) proposes high order Gram matrices to perform OOD detection by computing classconditional pairwise feature correlations between the test sample and the training set across the hidden layers of the network . Lee et al . ( 2018 ) assume that latent features of DNN models trained under the softmax score follow a class-conditional Gaussian mixture distribution with tied covariance matrix and different class-conditional mean vectors . They calculate the Mahalanobis distance between a test sample as a single estimator of the mean of a class-conditional Gaussian distribution with a tied covariance matrix estimated on the training set . The importance of each low-level component and hyperparameters are tuned using validation data . Ren et al . ( 2021 ) modifies this method to improve detection of near-OOD data . They fit the layer-wise background distribution with a Gaussian distribution with mean and standard calculated empirically from the training set . They subtract the Mahalanobis distance between the test example and this distribution from the score proposed in Lee et al . ( 2018 ) , reducing the importance of features shared by in- and out-of-distribution data . 2 BACKGROUND . Let X ⊆ Rd be the feature space ( continuous ) and Y a label space . Moreover , let pXY be the underlying unknown probability density function ( pdf ) over X × Y . We define the in-distribution training dataset as DN , { ( xi , yi ) } N i=1 ∼ pXY , where xi ∈ X is the input feature data , yi ∈ Y , { 1 , . . . , C } is the output class among C possible classes and N denotes the number of training samples . The training dataset is characterized by the joint pdf pXY with in-distribution marginals X ∼ pX and Y ∼ PY . The predictor denoted by fDN : X → Y is based on the inferred model PŶ |X , i.e. , fDN ( x ) ≡ fn ( x ; DN ) , arg maxy∈Y PŶ |X ( y|x ; DN ) . In order to model the underlying problem , we introduce an artificial binary random variable Z ∈ { 0 , 1 } indicating with z = 1 that the test sample x is OOD and otherwise , it is in-distribution . The open-world data can then be modeled as a mixture distribution pX|Z defined by pX|Z ( x|z = 0 ) , pX ( x ) , pX|Z ( x|z = 1 ) , qX ( x ) . ( 1 ) The intrinsic difficulty arises from the fact that very little can be assumed about the unknown distributions pX and qX , in particular for out-of-distribution .

Summary: The paper proposes a new group of methods for supervised OOD detection. In particular, the authors propose to use the Fisher-Rao distance between output distributions on the in-distribution data and test samples to detect OOD. The authors additionally propose to use Fisher-Rao distance in the hidden layer feature space, when possible (white-box setting). The method achieves strong empirical performance, improving upon standard baselines (such as Odin, Mahalanobis), especially in the white-box setting.