2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2022
Towards Robust Deep Learning using Entropic Losses
David Macêdo
Current deep learning solutions are well known for not informing whether they can reliably classify an example during inference. One of the most effective ways to build more reliab…
cs.LG2022★ 2 cited
Distinction Maximization Loss: Efficiently Improving Out-of-Distribution Detection and Uncertainty Estimation by Replacing the Loss and Calibrating
David Macêdo, Cleber Zanchettin, Teresa Ludermir
Building robust deterministic neural networks remains a challenge. On the one hand, some approaches improve out-of-distribution detection at the cost of reducing classification acc…
cs.LG2021
Enhanced Isotropy Maximization Loss: Seamless and High-Performance Out-of-Distribution Detection Simply Replacing the SoftMax Loss
David Macêdo, Teresa Ludermir
Current out-of-distribution detection approaches usually present special requirements (e.g., collecting outlier data and hyperparameter validation) and produce side effects (e.g.,…