Survey of methods for handling distribution shifts for robust computer vision
Survey of methods for handling distribution shifts for robust computer vision
Robust Computer Vision in an Ever-Changing World: A Survey of Techniques for Tackling Distribution Shifts
arXiv paper abstract https://arxiv.org/abs/2312.01540
arXiv PDF paper https://arxiv.org/pdf/2312.01540.pdf
... There is ... gap between ... computer vision models and ... the real world. One ... is ... distribution shift.
... In ... paper, ... discuss the identification of such a prominent gap, exploring the concept of distribution shift and its critical significance.
... 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.
... compare and contrast numerous AI models that are built for mitigating shifts in hidden stratification and spurious correlations, adversarial attack shift, and unseen data shifts.
... summarize the innovations and major contributions in the literature, give new perspectives toward robustness, and highlight the limitations of those proposed ideas ...
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