Data-Centric AI Paradigm Based on Application-Driven Fine-grained Dataset Design. (arXiv:2209.09449v2 [cs.CV] UPDATED)

Deep learning has a wide range of applications in industrial scenario, but
reducing false alarm (FA) remains a major difficulty. Optimizing network
architecture or network parameters is used to tackle this challenge in academic
circles, while ignoring the essential characteristics of data in application
scenarios, which often results in increased FA in new scenarios. In this paper,
we propose a novel paradigm for fine-grained design of datasets, driven by
industrial applications. We flexibly select positive and negative sample sets
according …

application arxiv data data-centric dataset design fine-grained paradigm