Search Results for author: Connor Anderson

Found 3 papers, 1 papers with code

Improving Fractal Pre-training

1 code implementation6 Oct 2021 Connor Anderson, Ryan Farrell

Challenging issues with large-scale image datasets become points of elegance for fractal pre-training: perfect label accuracy at zero cost; no need to store/transmit large image archives; no privacy/demographic bias/concerns of inappropriate content, as no humans are pictured; limitless supply and diversity of images; and the images are free/open-source.

Fair Comparison: Quantifying Variance in Resultsfor Fine-grained Visual Categorization

no code implementations7 Sep 2021 Matthew Gwilliam, Adam Teuscher, Connor Anderson, Ryan Farrell

From this analysis, we both highlight the importance of reporting and comparing methods based on information beyond overall accuracy, as well as point out techniques that mitigate variance in FGVC results.

Fine-Grained Visual Categorization Image Classification

Facing the Hard Problems in FGVC

no code implementations23 Jun 2020 Connor Anderson, Matt Gwilliam, Adam Teuscher, Andrew Merrill, Ryan Farrell

In fine-grained visual categorization (FGVC), there is a near-singular focus in pursuit of attaining state-of-the-art (SOTA) accuracy.

Fine-Grained Visual Categorization

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