Datasets:
Tasks:
Image Classification
Modalities:
Text
Formats:
webdataset
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
1M - 10M
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## Neglected Free Lunch – Learning Image Classifiers Using Annotation Byproducts | [Paper](https://arxiv.org/abs/2303.17595) Dongyoon Han<sup>1*</sup>, Junsuk Choe<sup>2*</sup>, Seonghyeok Chun<sup>3</sup>, John Joon Young Chung<sup>4</sup> Minsuk Chang<sup>5</sup>, Sangdoo Yun<sup>1</sup>, Jean Y. Song<sup>6</sup>, Seong Joon Oh<sup>7†</sup> <sub>\* Equal contribution</sub> <sub>†</sub> <sub> Corresponding author </sub> <sup>1</sup> <sub>NAVER AI LAB</sub> <sup>2</sup> <sub>Sogang University</sub> <sup>3</sup> <sub>Dante Company</sub> <sup>4</sup> <sub>University of Michigan</sub> <sup>5</sup> <sub>NAVER AI LAB, currently at Google</sub> <sup>6</sup> <sub>DGIST</sub> <sup>7</sup> <sub>University of Tübingen</sub> Supervised learning of image classifiers distills human knowledge into a parametric model *f* through pairs of images and corresponding labels (*X*,*Y*). We argue that this simple and widely used representation of human knowledge neglects rich au…0d5efe4 -
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