2021

Re-labeling ImageNet: from Single to Multi-Labels, from Global to Localized Labels

Yun, Sangdoo, Oh, Seong Joon, Heo, Byeongho et al.

Understand

ImageNet has been arguably the most popular image classification benchmark, but it is also the one with a significant level of label noise.

Recent studies have shown that many samples contain multiple classes, despite being assumed to be a single-label benchmark.
They have thus proposed to turn ImageNet evaluation into a multi-label task, with exhaustive multi-label annotations per image.
However, they have not fixed the training set, presumably because of a formidable annotation cost.

Built on

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Then

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Meal: Multi-model ensemble via adversarial learning
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Mingxing Tan and Quoc Le · 2019
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Fixing the train-test resolution discrepancy
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Billion-scale semi-supervised learning for image classification
Original
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Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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Rexnet: Diminishing representational bottleneck on convolutional neural network
Dongyoon Han, Sangdoo Yun, Byeongho Heo, and YoungJoon Yoo · 2021
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