1 citations · 1 across the 3 of their papers we have counts for
3 papers
RobustCLEVR: A Benchmark and Framework for Evaluating Robustness in Object-centric Learning
Nathan Drenkow, Mathias Unberath
Object-centric representation learning offers the potential to overcome limitations of image-level representations by explicitly parsing image scenes into their constituent compone…
Exploiting Large Neuroimaging Datasets to Create Connectome-Constrained Approaches for more Robust, Efficient, and Adaptable Artificial Intelligence
Erik C. Johnson, Brian S. Robinson, Gautam K. Vallabha +17
Despite the progress in deep learning networks, efficient learning at the edge (enabling adaptable, low-complexity machine learning solutions) remains a critical need for defense a…
Data AUDIT: Identifying Attribute Utility- and Detectability-Induced Bias in Task Models
Mitchell Pavlak, Nathan Drenkow, Nicholas Petrick +2
To safely deploy deep learning-based computer vision models for computer-aided detection and diagnosis, we must ensure that they are robust and reliable. Towards that goal, algorit…