Towards robust inference against distribution shifts in computer vision - Tang, Kaihua (Nanyang Technological University, 2021)
After a decade of prosperity, the development of machine learning based on deep neural networks (DNNs) seems to reach a new turning point. A variety of tasks and fields have proved that recklessly feeding a massive volume of data and increasing the model capacity would no longer bring us a panacea for all the problems. The ubiquitous bias in the model structures, long-tailed distributions, and optimization strategies stops the DNN from learning the underlying causal mechanisms, resulting in the catastrophic drop of performances when facing distribution shift problems like rare spatial layouts, misalignment between source domains and targeted domains, or adversarial perturbations.