Bilkent University
Department of Computer Engineering
CS 590/690 SEMINAR
Uncertainty Estimation in Deep Learning using Masked Autoencoders and Test-Time Training
Sepehr Maleki
Ph.D. Student
(Supervisor:Asst.Prof.Doruk Öner)
Computer Engineering Department
Bilkent University
Abstract: Deep learning models often make predictions without indicating their confidence or explaining sources of uncertainty, limiting their deployment in safety-critical applications. This thesis proposes a framework that separates two types of uncertainty: epistemic uncertainty (model uncertainty about its parameters) and aleatoric uncertainty (inherent data noise or ambiguity). We use a dual-task architecture with a shared encoder that performs both masked autoencoding and segmentation. For epistemic uncertainty, we introduce an SVD-based weight perturbation method that identifies the most important network weights using singular value decomposition and perturbs only those weights. By running multiple forward passes with different perturbations, we measure prediction variance as a direct indicator of model parameter uncertainty. For aleatoric uncertainty, we measure how long test-time training takes to converge on the reconstruction task longer convergence indicates noisier or more ambiguous input data. This approach provides spatial uncertainty maps showing where the model is uncertain and distinguishes between uncertainty caused by the model’s limited knowledge versus uncertainty inherent in the data itself. The framework enables practical applications such as identifying when expert review is needed, when data quality should be improved, and which samples would be most valuable for active learning in medical imaging, autonomous driving, and other critical domains.
DATE: April 06, Monday @ 15:30 Place: EA 502