  The problem of different training and test set class priors is addressed in
the context of CNN classifiers. We compare two different approaches to
estimating the new priors: an existing Maximum Likelihood Estimation approach
(optimized by an EM algorithm or by projected gradient descend) and a proposed
Maximum a Posteriori approach, which increases the stability of the estimate by
introducing a Dirichlet hyper-prior on the class prior probabilities.
Experimental results show a significant improvement on the fine-grained
classification tasks using 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. Estimation of the unknown test set priors
noticeably increases the accuracy on the PlantCLEF dataset, allowing a single
CNN model to achieve state-of-the-art results and outperform the
competition-winning ensemble of 12 CNNs. The proposed Maximum a Posteriori
estimation increases the prediction accuracy by 2.8% on PlantCLEF 2017 and by
1.8% on FGVCx Fungi, where the existing MLE method would lead to a decrease
accuracy.

Computer Vision and Pattern Recognition