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 investigates the robustness problem of computer vision model. To study the model robustness in a controlled setting, the author introduces three new robustness benchmarks: ImageNet-R, StreetView StoreFronts and DeepFashion Remixed. Each of them address different aspects of distribution drift in the real world. The author evaluates seven popular hypotheses on model robustness in the community on the three new datasets and has found counter-example for most of them. Based on those new results, the author concluded that model robustness problem is multi-variate in nature: no single solution could handle all aspects yet. And future work should be tested on multiple datasets to prove robustness. Moreover, the author also proposes a new data augmentation method using perturbed image-to-image deep learning model to generate visually diverse augmentations.