Divide and Ensemble: Progressively Learning for the Unknown
Hu Zhang Xin Shen Heming Du Huiqiang Chen Chen Liu Hongwei Sheng Qingzheng Xu MD Wahiduzzaman Khan Qingtao Yu Tianqing Zhu Scott Chapman Zi Huang Xin Yu
In the wheat nutrient deficiencies classification challenge, we present the
DividE and EnseMble (DEEM) method for progressive test data predictions. We
find that (1) test images are provided in the challenge; (2) samples are
equipped with their collection dates; (3) the samples of different dates show
notable discrepancies. Based on the findings, we partition the dataset into
discrete groups by the dates and train models on each divided group. We then
adopt the pseudo-labeling approach to label the test data and incorporate those
with high confidence into the training set. In pseudo-labeling, we leverage
models ensemble with different architectures to enhance the reliability of
predictions. The pseudo-labeling and ensembled model training are iteratively
conducted until all test samples are labeled. Finally, the separated models for
each group are unified to obtain the model for the whole dataset. Our method
achieves an average of 93.6\% Top-1 test accuracy~(94.0\% on WW2020 and 93.2\%
on WR2021) and wins the 1$st$ place in the Deep Nutrient Deficiency
Challenge~\footnote{https://cvppa2023.github.io/challenges/}.