NoFADE: Analyzing Diminishing Returns on CO2 Investment
Published in Climate Change with Machine Learning workshop at 35th Conference on Neural Information Processing Systems (NeurIPS2021-CCAI), 2021
Recommended citation: Andre Fu, Justin Tran, Andy Xie, Jonathan Spraggett, Elisa Ding, Chang-Won Lee, Kanav Singla, Mahdi S. Hosseini, Konstantinos N. Plataniotis. (2021). "NoFADE: Analyzing Diminishing Returns on CO2 Investment." Climate Change with Machine Learning workshop at 35th Conference on Neural Information Processing Systems (NeurIPS2021-CCAI). 1(1). [https://arxiv.org/abs/2111.14059](https://arxiv.org/abs/2111.14059)
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.
Recommended citation: Andre Fu, Justin Tran, Andy Xie, Jonathan Spraggett, Elisa Ding, Chang-Won Lee, Kanav Singla, Mahdi S. Hosseini, Konstantinos N. Plataniotis. (2021). "NoFADE: Analyzing Diminishing Returns on CO2 Investment." Climate Change with Machine Learning workshop at 35th Conference on Neural Information Processing Systems (NeurIPS2021-CCAI). 1(1).