179 citations · 275 across the 5 of their papers we have counts for
6 papers
Scaling Out-of-Distribution Detection for Real-World Settings
Dan Hendrycks, Steven Basart, Mantas Mazeika +5
Detecting out-of-distribution examples is important for safety-critical machine learning applications such as detecting novel biological phenomena and self-driving cars. However, e…
Learning Rich Representations For Structured Visual Prediction Tasks
Mohammadreza Mostajabi
We describe an approach to learning rich representations for images, that enables simple and effective predictors in a range of vision tasks involving spatially structured maps. Ou…
DIODE: A Dense Indoor and Outdoor DEpth Dataset
Igor Vasiljevic, Nick Kolkin, Shanyi Zhang +8
We introduce DIODE, a dataset that contains thousands of diverse high resolution color images with accurate, dense, long-range depth measurements. DIODE (Dense Indoor/Outdoor DEpth…
Regularizing Deep Networks by Modeling and Predicting Label Structure
Mohammadreza Mostajabi, Michael Maire, Gregory Shakhnarovich
We construct custom regularization functions for use in supervised training of deep neural networks. Our technique is applicable when the ground-truth labels themselves exhibit int…
Diverse Sampling for Self-Supervised Learning of Semantic Segmentation
Mohammadreza Mostajabi, Nicholas Kolkin, Gregory Shakhnarovich
We propose an approach for learning category-level semantic segmentation purely from image-level classification tags indicating presence of categories. It exploits localization cue…
Feedforward semantic segmentation with zoom-out features
Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich
We introduce a purely feed-forward architecture for semantic segmentation. We map small image elements (superpixels) to rich feature representations extracted from a sequence of ne…