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AI applications are becoming increasingly visible to the general public.There is a notable gap between the theoretical assumptions researchers makeabout computer vision models and the reality those models face when deployed inthe real world. One of the critical reasons for this gap is a challengingproblem known as distribution shift. Distribution shifts tend to vary withcomplexity of the data, dataset size, and application type. In our paper, wediscuss the identification of such a prominent gap, exploring the concept ofdistribution shift and its critical significance. We provide an in-depthoverview of various types of distribution shifts, elucidate their distinctions,and explore techniques within the realm of the data-centric domain employed toaddress them. Distribution shifts can occur during every phase of the machinelearning pipeline, from the data collection stage to the stage of training amachine learning model to the stage of final model deployment. As a result, itraises concerns about the overall robustness of the machine learning techniquesfor computer vision applications that are deployed publicly for consumers.Different deep learning models each tailored for specific type of data andtasks, architectural pipelines; highlighting how variations in datapreprocessing and feature extraction can impact robustness., data augmentationstrategies (e.g. geometric, synthetic and learning-based); demonstrating theirrole in enhancing model generalization, and training mechanisms (e.g. transferlearning, zero-shot) fall under the umbrella of data-centric methods. Each ofthese components form an integral part of the neural-network we analyzecontributing uniquely to strengthening model robustness against distributionshifts. We compare and contrast numerous AI models that are built formitigating shifts in hidden stratification and spurious correlations, ...

Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shifts