Natural Attribute-based Shift Detection

1 INTRODUCTION . Deep learning has significantly improved the performance in various domains such as computer vision , natural language processing , and healthcare ( Bojarski et al. , 2016 ; Mikolov et al. , 2013 ; Esteva et al. , 2016 ) . However , it has been reported that the deep classifiers make unreliable predictions on samples drawn from a different distribution than the training distribution ( Amodei et al. , 2016 ; Hendrycks & Gimpel , 2017 ; Nguyen et al. , 2015 ) . Detection of unreliable predictions is important to build a robust model but it is relatively hard when the test distribution is gradually shifting due to a natural attribute since it is difficult to determine whether the classifier fails when the shift is not significant . These shifts occur in the real-world as a result of a change in specific attribute . For example , a clinical text-based diagnosis classifier trained in 2021 will gradually encounter increasingly shifted samples as time flows , since writing styles change and new terms are introduced in time . Detection of such samples is a vital task especially in safety-critical systems , such as autonomous vehicle control or medical diagnosis , where wrong predictions can lead to dire consequences . To this end , we take a step forward by proposing a new task of detecting samples shifted by a natural attribute ( e.g. , age , time ) that can easily be observed in the real-world setting . We refer to such shifts as Natural Attribute-based Shifts ( NAtS ) , and the task of detecting them as NAtS detection . Detection of NAtS is both different from , and also more challenging than out-of-distribution ( OOD ) detection ( Hendrycks & Gimpel , 2017 ; Liang et al. , 2018 ; Lee et al. , 2018 ; Chandramouli & Sageev , 2020 ) , which typically evaluates the detection methods with a clearly distinguished in-distribution ( ID ) samples and OOD samples ( e.g. , CIFAR10 as ID and SVHN as OOD , which have disjoint labels ) . In contrast , we aim to detect samples from a natural attribute-based shift within the same label space . Since NAtS samples share more features with the ID than the typical OOD samples do , identifying the former is expected to be more challenging than the latter . Although OOD detection has some relevance to NAtS detection , comprehensive evaluation of the existing OOD detection methods on the natural attribute-based shift is an unexplored territory . Therefore , in this paper , we perform an extensive evaluation of representative OOD methods on NAtS samples . Depending on the task environment , NAtS detection can be pursued in parallel to domain generalization ( Seo et al. , 2020 ; Gulrajani & Lopez-Paz , 2020 ; Carlucci et al. , 2019 ) , which aims to overcome domain shifts ( e.g. , image classifier adapting to sketches , photos , art paintings , etc. ) . For example , an X-ray-based diagnosis model should detect images of unusual brightness so that the X-ray ma- chine can be properly configured , and the diagnosis model can perform in the optimal setting . In other cases , domain generalization can be preferred , such as when we expect the classifier to be deployed in a less controlled environment ( e.g. , online image classifier ) for non-safety critical tasks . In this paper , we formalize NAtS detection to enhance the reliability of real-world decision systems . Since there exists no standard dataset for this task , we create a new benchmark dataset in the vision , text , and medical domain by adjusting the natural attributes ( e.g. , age , time , and brightness ) of the ID dataset . Then we conduct an extensive evaluation of representative confidence- and distance-based OOD methods on our datasets and observe that none of the methods perform consistently across all NAtS datasets . After a careful analysis on where NAtS samples reside in the feature space and its impact on the distance- and confidence-based OOD detection performance , we identify the root cause of the inconsistent performance . Following this observation , we define three general NAtS categories based on two criteria : the distance between NAtS samples and the decision boundary , the distance between NAtS samples , and the ID data . Finally , we conduct an additional experiment to demonstrate that a simple modification to the negative log-likelihood training objective can dramatically help the Mahalanobis detector ( Lee et al. , 2018 ) , a distance-based OOD detection method , generalize to all NAtS categories . We also compare our results with various baselines and show that our proposed modification outperforms the baselines and is effective across the three NAtS datasets . In summary , the contributions of this paper are as follows : • We define a new task , Natural Attribute-based Shift detection ( NAtS detection ) , which aims to detect the samples from a distribution shifted by some natural attribute . We create a new benchmark dataset and provide them to encourage further research on evaluating NAtS detection . • To the best of our knowledge , this is the first work to conduct a comprehensive evaluation of the OOD detection methods on shifts based on natural attributes , and discover that none of the OOD methods perform consistently across all NAtS scenarios . • We provide novel analysis based on the location of shifted samples in the feature space and the performance of existing OOD detection methods . Based on the analysis , we split NAtS samples into three categories . • We demonstrate that a simple yet effective modification to the training objective for deep classifiers enables consistent OOD detection performances for all NAtS categories . 2 NATURAL ATTRIBUTE-BASED SHIFT DETECTION . We now formalize a new task , NAtS detection , which aims to enhance the reliability of real-world decision systems by detecting samples from NAtS . We address this task in the classification problems . Let DI = { X , Y } denote the in-distribution data , which is composed of N training samples with inputs X = { x1 , ... , xN } and labels Y = { y1 , ... , yN } . Specifically , xi ∈ Rd represents a d-dimensional input vector , and yi ∈ K represents its corresponding label where K = { 1 , ... , K } is a set of class labels . The discriminative model fθ : X → Y learns with ID dataset DI to assign label yi for each xi . In the NAtS detection setting , we assume that an in-distribution sample consists of attributes , and some of the attributes can be shifted in the test time due to natural causes such as time , age , or brightness . When a particular attribute A ( e.g. , age ) , which has a value of a ( e.g. , 16 ) , is shifted by the degree of δ , the shifted distribution can be denoted as DA=a+δS = { X ′ , Y ′ } . X ′ = { x′1 , ... , x′M } and Y ′ = { y′1 , ... , y′M } represents the M shifted samples and labels respectively . Importantly , in the NAtS setting , although the test distribution is changed from the ID , the label space is preserved as K , which is the set of class labels in DI . In the test time , the model fθ might encounter the sample x′ from a shifted data DA=a+δS , and it should be able to identify that the attribute-shifted sample is not from the ID . 3 NATS DATASET DESCRIPTION . In this section , we describe three benchmark datasets which have a controllable attribute for simulating realistic distribution shifts . Since there exists no standard dataset for NAtS detection , we create new benchmark datasets using existing datasets by adjusting natural attributes in order to reflect real-world scenarios . We carefully select datasets from vision , language , and medical domains containing natural attributes ( e.g. , year , age , and brightness ) , which allows us to naturally split the samples . By grouping samples based on these attributes , we can induce natural attribute-based distribution shifts as described below . Image . We use the UTKFace dataset ( Zhang et al. , 2017 ) which consists of over 20 , 000 face images with annotations of age , gender , and ethnicity . As shown in Figure 1 , we can visually observe that the facial images vary with age . Therefore , we set the 1 , 282 facial images of 26 years old age as DI . For creating the NAtS dataset , we vary the age of UTKFace dataset . To obtain an equal number of samples in the NAtS dataset , the age groups that has less than 200 images are merged into one group until it has 200 samples . Finally , 15 groups DageS are produced for the NAtS datasets , varying the ages from 25 to 1 ( i.e. , Dage=25S , D age=24 S , . . . , D age=1 S ) . Text . We use the Amazon Review dataset ( He & McAuley , 2016 ; McAuley et al. , 2015 ) which contains product reviews from Amazon . We consider the product category ” book ” and group its reviews based on the year to reflect the distributional shift across time . We obtain 9 groups with each group containing reviews from the year between 2005 and 2014 . Then , the group with 24 , 000 reviews posted in 2005 is set as DI , and the groups with reviews after 2005 as DyearS ( i.e. , Dyear=2006S , D year=2007 S , . . . , D year=2014 S ) . Each D year S group contains 1500 positive reviews and 1500 negative reviews . We observed that as we move ahead in time , the average length of a review gets shorter and it uses more adjectives than previous years . Due to the space constraint , we provide a detailed analysis of the dataset in the Section B of the Appendix . Medical . We use the RSNA Bone Age dataset ( Halabi et al. , 2019 ) , a real-world dataset that contains left-hand X-ray images of the patient , along with their gender and age ( 0 to 20 years ) . We consider patients in the age group of 10 to 12 years for our dataset . To reflect diverse X-ray imaging set-ups in the hospital , we varied the brightness factor between 0 and 4.5 and form 16 different dataset DbrightnessS ( i.e. , D brightness=0.0 S , D brightness=0.2 S , . . . , D brightness=4.5 S ) , and each group contains Xray images of 200 males and 200 females . Figure 1 presents X-ray images with different levels of brightness with realistic and continuous distribution shifts . In-distribution data DI is composed of 3 , 000 images of brightness factor 1.0 ( unmodified images ) . 4 CAN OOD DETECTION METHODS ALSO DETECT NATS ? . In this section , we briefly discuss about OOD detection methods and conduct an extensive evaluation of OOD detection methods on our proposed benchmark datasets . 4.1 OOD DETECTION METHODS . In this work , we use three widely-used post-hoc and modality-agnostic OOD detection methods . We use maximum of softmax probability ( MSP ) ( Hendrycks & Gimpel , 2017 ) and ODIN ( Liang et al. , 2018 ) as confidence-based OOD detection baselines , and Mahalanobis detector ( Lee et al. , 2018 ) as distance-based OOD detection baseline . Note that ODIN and Mahalanobis detector assume the availability of OOD validation dataset to tune their hyperparameters . However , for all our experiments , we use variants of the above methods that do not access the OOD validation dataset as Hsu et al . ( 2020 ) . The exact equations and details of how each OOD detection method assigns an OOD score to a given sample is provided in Section A of the Appendix .

Summary: The paper identifies the problem of a Natural Attributed-based Shift (NAS) in the data distribution affecting Deep Learning (DL) systems designed for the task of interest. An example may be a DL system for X-ray based diagnosis being able to handle a different brightness that it was trained for. The paper argues that it is similar to Out-of-Distribution (OOD) data but instead of the difference being in the label space (CIFAR vs SVHN) the label space is similar but the test data distribution is shifted due to a systemic shift in an important data attribute (brightness of image in the X-ray example above). The paper claims the following contributions: - A new task of NAS detection and datasets to study it. - A comprehensive evaluation of OOD methods on the NAS dataset and demonstration that none of them perform consistently across all NAS scenarios. - A novel analysis based on the location of shifted samples in the feature space and performance of existing OOD methods. - Demonstrate that a simple modification to training objective for deep classifiers enables consistent OOD performance on all NAS scenarios.