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Found 3 papers, 1 papers with code
Contrastive Classification and Representation Learning with Probabilistic Interpretation
In this work, we investigate the possibility of learning both the representation and the classifier using one objective function that combines the robustness of contrastive learning and the probabilistic interpretation of cross entropy loss.
The Hitchhiker's Guide to Prior-Shift Adaptation
In many computer vision classification tasks, class priors at test time often differ from priors on the training set.
Improving CNN classifiers by estimating test-time priors
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.
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