2 citations · 2 across the 7 of their papers we have counts for
7 papers
Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning
Jakub Paplhám, Willem Waegeman, Eyke Hüllermeier +1
Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning. However, the Bayes-optimal decision strategies for these tasks…
Few-Shot Personalized Age Estimation
Jakub Paplhám, Vojtěch Franc, Artem Moroz
Existing age estimation methods treat each face as an independent sample, learning a global mapping from appearance to age. This ignores a well-documented phenomenon: individuals a…
Photo Dating by Facial Age Aggregation
Jakub Paplham, Vojtech Franc
We introduce a novel method for Photo Dating which estimates the year a photograph was taken by leveraging information from the faces of people present in the image. To facilitate…
Epistemic Reject Option Prediction
Vojtech Franc, Jakub Paplham
In high-stakes applications, predictive models must not only produce accurate predictions but also quantify and communicate their uncertainty. Reject-option prediction addresses th…
SCOD: From Heuristics to Theory
Vojtech Franc, Jakub Paplham, Daniel Prusa
This paper addresses the problem of designing reliable prediction models that abstain from predictions when faced with uncertain or out-of-distribution samples - a recently propose…
Reject option models comprising out-of-distribution detection
Vojtech Franc, Daniel Prusa, Jakub Paplham
The optimal prediction strategy for out-of-distribution (OOD) setups is a fundamental question in machine learning. In this paper, we address this question and present several cont…
A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark
Jakub Paplham, Vojtech Franc
Comparing different age estimation methods poses a challenge due to the unreliability of published results stemming from inconsistencies in the benchmarking process. Previous studi…