Ensemble Meta-Learning for Few-Shot Soot Density Recognition

In each petrochemical plant around the world, the flare stack as a requisite facility produces a large amount of soot due to the incomplete combustion of flare gas, and this strongly endangers air quality and human health. Despite severe damages, the abovementioned abnormal conditions rarely occur, and, thus, only few-shot samples are available. To address such difficulty, in this article, we design an image-based flare soot density recognition network (FSDR-Net) via a new ensemble meta-learning technology. More particularly, we first train a deep convolutional neural network (CNN) by applying the model-agnostic meta-learning algorithm on a variety of learning tasks that are relevant to the flare soot recognition so as to obtain the general-purpose optimized initial parameters (GOIP). Second, for the new task of recognizing the flare soot density via only few-shot instances, a new ensemble is developed to selectively aggregate several predictions that are generated based on a wide range of learning rates and a small number of gradient steps. Results of experiments conducted on the density recognition of flare soot corroborate the superiority of our proposed FSDR-Net as compared with the popular and state-of-the-art deep CNNs.