A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation

arXiv:2402.18402v1 Announce Type: new
Abstract: In multimedia understanding tasks, corrupted samples pose a critical challenge, because when fed to machine learning models they lead to performance degradation. In the past, three groups of approaches have been proposed to handle noisy data: i) enhancer and denoiser modules to improve the quality of the noisy data, ii) data augmentation approaches, and iii) domain adaptation strategies. All the aforementioned approaches come with drawbacks that limit their applicability; the first has high computational costs …

abstract arxiv challenge cs.cv data fed machine machine learning machine learning models modular modules multimedia networks parametric performance robustness samples tasks type understanding via