NoFADE: Analyzing Diminishing Returns on CO2 Investment (Papers Track)
Andre Fu (University of Toronto); Justin B Tran (University of Toronto); Andy Xie (University of Toronto); Jonathan T Spraggett (University of Toronto); Elisa Ding (University of Toronto); Chang-Won Lee (University of Toronto); Kanav Singla (University of Toronto); Mahdi S. Hosseini (University of New Brunswick); Konstantinos N Plataniotis (UofT)
Abstract
Climate change continues to be a pressing issue that currently affects society at-large. It is important that we as a society, including the Computer Vision (CV) community take steps to limit our impact on the environment. In this paper, we (a) analyze the effect of diminishing returns on CV methods, and (b) propose a \textit{``NoFADE''}: a novel entropy-based metric to quantify model--dataset--complexity relationships. We show that some CV tasks are reaching saturation, while others are almost fully saturated. In this light, NoFADE allows the CV community to compare models and datasets on a similar basis, establishing an agnostic platform.