no code implementations • 7 Nov 2022 • Rahaf Aljundi, Yash Patel, Milan Sulc, Daniel Olmeda, Nikolay Chumerin
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.
1 code implementation • 22 Jun 2021 • Tomas Sipka, Milan Sulc, Jiri Matas
In many computer vision classification tasks, class priors at test time often differ from priors on the training set.
no code implementations • 21 May 2018 • Milan Sulc, Jiri Matas
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.