13 citations · 20 across the 3 of their papers we have counts for
4 papers
Improving Fractal Pre-training
Connor Anderson, Ryan Farrell
The deep neural networks used in modern computer vision systems require enormous image datasets to train them. These carefully-curated datasets typically have a million or more ima…
Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization
Matthew Gwilliam, Adam Teuscher, Connor Anderson +1
For the task of image classification, researchers work arduously to develop the next state-of-the-art (SOTA) model, each bench-marking their own performance against that of their p…
Optimally Summarizing Data by Small Fact Sets for Concise Answers to Voice Queries
Immanuel Trummer, Connor Anderson
Our goal is to find combinations of facts that optimally summarize data sets. We consider this problem in the context of voice query interfaces for simple, exploratory data analysi…
Facing the Hard Problems in FGVC
Connor Anderson, Matt Gwilliam, Adam Teuscher +2
In fine-grained visual categorization (FGVC), there is a near-singular focus in pursuit of attaining state-of-the-art (SOTA) accuracy. This work carefully analyzes the performance…