40 citations · 246 across the 29 of their papers we have counts for
6 papers · 1 filter
A Novel Unsupervised Post-Processing Calibration Method for DNNS with Robustness to Domain Shift
Azadeh Sadat Mozafari, Hugo Siqueira Gomes, Christian Gagne
The uncertainty estimation is critical in real-world decision making applications, especially when distributional shift between the training and test data are prevalent. Many calib…
Deep Parametric Indoor Lighting Estimation
Marc-André Gardner, Yannick Hold-Geoffroy, Kalyan Sunkavalli +2
We present a method to estimate lighting from a single image of an indoor scene. Previous work has used an environment map representation that does not account for the localized na…
Toward Metrics for Differentiating Out-of-Distribution Sets
Mahdieh Abbasi, Changjian Shui, Arezoo Rajabi +2
Vanilla CNNs, as uncalibrated classifiers, suffer from classifying out-of-distribution (OOD) samples nearly as confidently as in-distribution samples. To tackle this challenge, som…
Unsupervised Temperature Scaling: An Unsupervised Post-Processing Calibration Method of Deep Networks
Azadeh Sadat Mozafari, Hugo Siqueira Gomes, Wilson Leão +1
The great performances of deep learning are undeniable, with impressive results over a wide range of tasks. However, the output confidence of these models is usually not well-calib…
A Principled Approach for Learning Task Similarity in Multitask Learning
Changjian Shui, Mahdieh Abbasi, Louis-Émile Robitaille +2
Multitask learning aims at solving a set of related tasks simultaneously, by exploiting the shared knowledge for improving the performance on individual tasks. Hence, an important…
Learning of Image Dehazing Models for Segmentation Tasks
Sébastien de Blois, Ihsen Hedhli, Christian Gagné
To evaluate their performance, existing dehazing approaches generally rely on distance measures between the generated image and its corresponding ground truth. Despite its ability…