22 citations · 31 across the 8 of their papers we have counts for
8 papers
On Spectral Properties of Gradient-based Explanation Methods
Amir Mehrpanah, Erik Englesson, Hossein Azizpour
Understanding the behavior of deep networks is crucial to increase our confidence in their results. Despite an extensive body of work for explaining their predictions, researchers…
Medical Image Segmentation with SAM-generated Annotations
Iira Häkkinen, Iaroslav Melekhov, Erik Englesson +2
The field of medical image segmentation is hindered by the scarcity of large, publicly available annotated datasets. Not all datasets are made public for privacy reasons, and creat…
Indirectly Parameterized Concrete Autoencoders
Alfred Nilsson, Klas Wijk, Sai bharath chandra Gutha +7
Feature selection is a crucial task in settings where data is high-dimensional or acquiring the full set of features is costly. Recent developments in neural network-based embedded…
Logistic-Normal Likelihoods for Heteroscedastic Label Noise
Erik Englesson, Amir Mehrpanah, Hossein Azizpour
A natural way of estimating heteroscedastic label noise in regression is to model the observed (potentially noisy) target as a sample from a normal distribution, whose parameters c…
Deep Double Descent via Smooth Interpolation
Matteo Gamba, Erik Englesson, Mårten Björkman +1
The ability of overparameterized deep networks to interpolate noisy data, while at the same time showing good generalization performance, has been recently characterized in terms o…
Consistency Regularization Can Improve Robustness to Label Noise
Erik Englesson, Hossein Azizpour
Consistency regularization is a commonly-used technique for semi-supervised and self-supervised learning. It is an auxiliary objective function that encourages the prediction of th…
Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels
Erik Englesson, Hossein Azizpour
Prior works have found it beneficial to combine provably noise-robust loss functions e.g., mean absolute error (MAE) with standard categorical loss function e.g. cross entropy (CE)…
Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation
Erik Englesson, Hossein Azizpour
In this work we aim to obtain computationally-efficient uncertainty estimates with deep networks. For this, we propose a modified knowledge distillation procedure that achieves sta…