  AI applications are becoming increasingly visible to the general public.
There is a notable gap between the theoretical assumptions researchers make
about computer vision models and the reality those models face when deployed in
the real world. One of the critical reasons for this gap is a challenging
problem known as distribution shift. Distribution shifts tend to vary with
complexity of the data, dataset size, and application type. In our paper, we
discuss the identification of such a prominent gap, exploring the concept of
distribution shift and its critical significance. We provide an in-depth
overview of various types of distribution shifts, elucidate their distinctions,
and explore techniques within the realm of the data-centric domain employed to
address them. Distribution shifts can occur during every phase of the machine
learning pipeline, from the data collection stage to the stage of training a
machine learning model to the stage of final model deployment. As a result, it
raises concerns about the overall robustness of the machine learning techniques
for computer vision applications that are deployed publicly for consumers.
Different deep learning models each tailored for specific type of data and
tasks, architectural pipelines; highlighting how variations in data
preprocessing and feature extraction can impact robustness., data augmentation
strategies (e.g. geometric, synthetic and learning-based); demonstrating their
role in enhancing model generalization, and training mechanisms (e.g. transfer
learning, zero-shot) fall under the umbrella of data-centric methods. Each of
these components form an integral part of the neural-network we analyze
contributing uniquely to strengthening model robustness against distribution
shifts. We compare and contrast numerous AI models that are built for
mitigating shifts in hidden stratification and spurious correlations, ...
