Improving CNN classifiers by estimating test-time priors

The problem of different training and test set class priors is addressed in the context of CNN classifiers. An EM-based algorithm for test-time class priors estimation is evaluated on fine-grained computer vision problems for both the batch and on-line situations. Experimental results show a significant improvement on the fine-grained classification tasks using the known evaluation-time priors, increasing the top-1 accuracy by 4.0% on the FGVC iNaturalist 2018 validation set and by 3.9% on the FGVCx Fungi 2018 validation set. Iterative estimation of test-time priors on the PlantCLEF 2017 dataset increased the image classification accuracy by 3.4%, allowing a single CNN model to achieve state-of-the-art results and outperform the competition-winning ensemble of 12 CNNs.