1 code implementation • NeurIPS 2023 • Tyler LaBonte, Vidya Muthukumar, Abhishek Kumar
In this work, we examine this impractical requirement and find that last-layer retraining can be surprisingly effective with no group annotations (other than for model selection) and only a handful of class annotations.
1 code implementation • 11 Jul 2022 • Tyler LaBonte, Yale Song, Xin Wang, Vibhav Vineet, Neel Joshi
A critical object detection task is finetuning an existing model to detect novel objects, but the standard workflow requires bounding box annotations which are time-consuming and expensive to collect.
1 code implementation • 23 Oct 2019 • Tyler LaBonte, Carianne Martinez, Scott A. Roberts
The geometric uncertainty maps generated by our BCNN capture distributions of sigmoid values that are interpretable as confidence intervals, critical for applications that rely on deep learning for high-consequence decisions.