A Critical Analysis of Distribution Shift

1 INTRODUCTION . While the research community must create robust models that generalize to new scenarios , the robustness literature ( Dodge and Karam , 2017 ; Geirhos et al. , 2020 ) lacks consensus on evaluation benchmarks and contains many dissonant hypotheses . Hendrycks et al . ( 2020a ) find that many recent language models are already robust to many forms of distribution shift , while Yin et al . ( 2019 ) and Geirhos et al . ( 2019 ) find that vision models are largely fragile and argue that data augmentation offers one solution . In contrast , Taori et al . ( 2020 ) provide results suggesting that using pretraining and improving in-distribution test set accuracy improve natural robustness , whereas other methods do not . In this paper we articulate and systematically study seven robustness hypotheses . The first four hypotheses concern methods for improving robustness , while the last three hypotheses concern abstract properties about robustness . These hypotheses are as follows . • Larger Models : increasing model size improves robustness ( Hendrycks and Dietterich , 2019 ; Xie and Yuille , 2020 ) . • Self-Attention : adding self-attention layers to models improves robustness ( Hendrycks et al. , 2019b ) . • Diverse Data Augmentation : robustness can increase through data augmentation ( Yin et al. , 2019 ) . • Pretraining : pretraining on larger and more diverse datasets improves robustness ( Orhan , 2019 ; Hendrycks et al. , 2019a ) . • Texture Bias : convolutional networks are biased towards texture , which harms robustness ( Geirhos et al. , 2019 ) . • Only IID Accuracy Matters : accuracy on independent and identically distributed test data entirely determines natural robustness . • Synthetic 6=⇒ Real : synthetic robustness interventions including diverse data augmentations do not help with robustness on real-world distribution shifts ( Taori et al. , 2020 ) . It has been difficult to arbitrate these hypotheses because existing robustness datasets preclude the possibility of controlled experiments by varying multiple aspects simultaneously . For instance , Texture Bias was initially investigated with synthetic distortions ( Geirhos et al. , 2018 ) , which conflicts with the Synthetic 6=⇒ Real hypothesis . On the other hand , natural distribution shifts often affect many factors ( e.g. , time , camera , location , etc . ) simultaneously in unknown ways ( Recht et al. , 2019 ; Hendrycks et al. , 2019b ) . Existing datasets also lack diversity such that it is hard to extrapolate which methods will improve robustness more broadly . To address these issues and test the seven hypotheses outlined above , we introduce three new robustness benchmarks and a new data augmentation method . First we introduce ImageNet-Renditions ( ImageNet-R ) , a 30,000 image test set containing various renditions ( e.g. , paintings , embroidery , etc . ) of ImageNet object classes . These renditions are naturally occurring , with textures and local image statistics unlike those of ImageNet images , allowing us to more cleanly separate the Texture Bias and Synthetic 6=⇒ Real hypotheses . Next , we investigate natural shifts in the image capture process with StreetView StoreFronts ( SVSF ) and DeepFashion Remixed ( DFR ) . SVSF contains business storefront images taken from Google Streetview , along with metadata allowing us to vary location , year , and even the camera type . DFR leverages the metadata from DeepFashion2 ( Ge et al. , 2019 ) to systematically shift object occlusion , orientation , zoom , and scale at test time . Both SVSF and DFR provide distribution shift controls and do not alter texture , which remove possible confounding variables affecting prior benchmarks . Finally , we contribute DeepAugment to increase robustness to some new types of distribution shift . This augmentation technique uses image-to-image neural networks for data augmentation , not data-independent Euclidean augmentations like image shearing or rotating as in previous work . DeepAugment achieves state-of-the-art robustness on our newly introduced ImageNet-R benchmark and a corruption robustness benchmark . DeepAugment can also be combined with other augmentation methods to outperform a model pretrained on 1000× more labeled data . After examining our results on these three datasets and others , we can rule out several of the above hypotheses while strengthening support for others . As one example , we find that synthetic data augmentation robustness interventions improve accuracy on ImageNet-R and real-world image blur distribution shifts , providing clear counterexamples to Synthetic 6=⇒ Real while lending support to the Diverse Data Augmentation and Texture Bias hypotheses . In the conclusion , we summarize the various strands of evidence for and against each hypothesis . Across our many experiments , we do not find a general method that consistently improves robustness , and some hypotheses require additional qualifications . While robustness is often spoken of and measured as a single scalar property like accuracy , our investigations suggest that robustness is not so simple . In light of our results , we hypothesize in the conclusion that robustness is multivariate . 2 RELATED WORK . Robustness Benchmarks . Recent works ( Hendrycks and Dietterich , 2019 ; Recht et al. , 2019 ; Hendrycks et al. , 2020a ) have begun to characterize model performance on out-of-distribution ( OOD ) data with various new test sets , with dissonant findings . For instance , Hendrycks et al . ( 2020a ) demonstrate that modern language processing models are moderately robust to numerous naturally occurring distribution shifts , and that Only IID Accuracy Matters is inaccurate for natural language tasks . For image recognition , Hendrycks and Dietterich ( 2019 ) analyze image models and show that they are sensitive to various simulated image corruptions ( e.g. , noise , blur , weather , JPEG compression , etc . ) from their “ ImageNet-C ” benchmark . Recht et al . ( 2019 ) reproduce the ImageNet ( Russakovsky et al. , 2015 ) validation set for use as a benchmark of naturally occurring distribution shift in computer vision . Their evaluations show a 11-14 % drop in accuracy from ImageNet to the new validation set , named ImageNetV2 , across a wide range of architectures . Taori et al . ( 2020 ) use ImageNetV2 to measure natural robustness and dismiss Diverse Data Augmentation . Recently , Engstrom et al . ( 2020 ) identify statistical biases in ImageNetV2 ’ s construction , and they estimate that reweighting ImageNetV2 to correct for these biases results in a less substantial 3.6 % drop . Data Augmentation . Geirhos et al . ( 2019 ) ; Yin et al . ( 2019 ) ; Hendrycks et al . ( 2020b ) demonstrate that data augmentation can improve robustness on ImageNet-C . The space of augmentations that help robustness includes various types of noise ( Madry et al. , 2017 ; Rusak et al. , 2020 ; Lopes et al. , 2019 ) , highly unnatural image transformations ( Geirhos et al. , 2019 ; Yun et al. , 2019 ; Zhang et al. , 2017 ) , or compositions of simple image transformations such as Python Imaging Library operations ( Cubuk et al. , 2018 ; Hendrycks et al. , 2020b ) . Some of these augmentations can improve accuracy on in-distribution examples as well as on out-of-distribution ( OOD ) examples . 3 NEW BENCHMARKS . In order to evaluate the seven robustness hypotheses , we introduce three new benchmarks that capture new types of naturally occurring distribution shifts . ImageNet-Renditions ( ImageNet-R ) is a newly collected test set intended for ImageNet classifiers , whereas StreetView StoreFronts ( SVSF ) and DeepFashion Remixed ( DFR ) each contain their own training sets and multiple test sets . SVSF and DFR split data into a training and test sets based on various image attributes stored in the metadata . For example , we can select a test set with images produced by a camera different from the training set camera . We now describe the structure and collection of each dataset . 3.1 IMAGENET-RENDITIONS ( IMAGENET-R ) . While current classifiers can learn some aspects of an object ’ s shape ( Mordvintsev et al. , 2015 ) , they nonetheless rely heavily on natural textural cues ( Geirhos et al. , 2019 ) . In contrast , human vision can process abstract visual renditions . For example , humans can recognize visual scenes from line drawings as quickly and accurately as they can from photographs ( Biederman and Ju , 1988 ) . Even some primates species have demonstrated the ability to recognize shape through line drawings ( Itakura , 1994 ; Tanaka , 2006 ) . To measure generalization to various abstract visual renditions , we create the ImageNet-Rendition ( ImageNet-R ) dataset . ImageNet-R contains various artistic renditions of object classes from the original ImageNet dataset . Note the original ImageNet dataset discouraged such images since annotators were instructed to collect “ photos only , no painting , no drawings , etc. ” ( Deng , 2012 ) . We do the opposite . Data Collection . ImageNet-R contains 30,000 image renditions for 200 ImageNet classes . We choose a subset of the ImageNet-1K classes , following Hendrycks et al . ( 2019b ) , for several reasons . A handful ImageNet classes already have many renditions , such as “ triceratops. ” We also choose a subset so that model misclassifications are egregious and to reduce label noise . The 200 class subset was also chosen based on rendition prevalence , as “ strawberry ” renditions were easier to obtain than “ radiator ” renditions . Were we to use all 1,000 ImageNet classes , annotators would be pressed to distinguish between Norwich terrier renditions as Norfolk terrier renditions , which is difficult . We collect images primarily from Flickr and use queries such as “ art , ” “ cartoon , ” “ graffiti , ” “ embroidery , ” “ graphics , ” “ origami , ” “ painting , ” “ pattern , ” “ plastic object , ” “ plush object , ” “ sculpture , ” “ line drawing , ” “ tattoo , ” “ toy , ” “ video game , ” and so on . Images are filtered by Amazon MTurk annotators using a modified collection interface from ImageNetV2 ( Recht et al. , 2019 ) . For instance , after scraping Flickr images with the query “ lighthouse cartoon , ” we have MTurk annotators select true positive lighthouse renditions . Finally , as a second round of quality control , graduate students manually filter the resulting images and ensure that individual images have correct labels and do not contain multiple labels . Examples are depicted in Figure 2 . ImageNet-R also includes the line drawings from Wang et al . ( 2019 ) , excluding horizontally mirrored duplicate images , pitch black images , and images from the incorrectly collected “ pirate ship ” class . 3.2 STREETVIEW STOREFRONTS ( SVSF ) . Computer vision applications often rely on data from complex pipelines that span different hardware , times , and geographies . Ambient variations in this pipeline may result in unexpected performance degradation , such as degradations experienced by health care providers in Thailand deploying laboratory-tuned diabetic retinopathy classifiers in the field ( Beede et al. , 2020 ) . In order to study the effects of shifts in the image capture process we collect the StreetView StoreFronts ( SVSF ) dataset , a new image classification dataset sampled from Google StreetView imagery ( Anguelov et al. , 2010 ) focusing on three distribution shift sources : country , year , and camera . Data Collection . SVSF consists of cropped images of business store fronts extracted from StreetView images by an object detection model . Each store front image is assigned the class label of the associated Google Maps business listing through a combination of machine learning models and human annotators . We combine several visually similar business types ( e.g . drugstores and pharmacies ) for a total of 20 classes , listed Appendix B. Splitting the data along the three metadata attributes of country , year , and camera , we create one training set and five test sets . We sample a training set and an in-distribution test set ( 200K and 10K images , respectively ) from images taken in US/Mexico/Canada during 2019 using a “ new ” camera system . We then sample four OOD test sets ( 10K images each ) which alter one attribute at a time while keeping the other two attributes consistent with the training distribution . Our test sets are year : 2017 , 2018 ; country : France ; and camera : “ old . ”

Summary: This paper provides a empirical study on the robustness of image classification models to distributions shifts. The authors construct three benchmark datasets that control for effects like artistic renditions of common classes, view-point changes, and geographic shifts (among others). The datasets are then used to test various hypotheses regarding robustness enhancing measures empirically. The authors additionally propose a novel augmentation scheme, that uses deep image processing networks together with random perturbations of their weights to synthesize distorted image samples.