What Does Classifying More Than 10,000 Image Categories Tell Us? What Does Classifying More Than 10,000 Image Categories Tell Us?
Paper summary In this paper the authors experiment with 10,000 image classes based on ImageNet. As ImageNet is based on Wordnet, they have a semantic tree of the categories. It should be noted that this paper is from 2010. Hence before AlexNet. They don't use CNNs in this paper. ## Key findings * A relationship between visual similarity and semantic similarity exists * Classification can be improved by exploiting semantic hierarchy * Computational difficulties with 10,000 classes * More classes -> lower mean accuracy ## See also * [What makes ImageNet good for transfer learning?](https://arxiv.org/abs/1608.08614) ([slides](https://www.dropbox.com/s/vfmncjnyh57glkc/NIPS_LSCVS_ImageNet%20Analysis.pdf?dl=0))
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What Does Classifying More Than 10,000 Image Categories Tell Us?
Jia Deng and Alexander C. Berg and Kai Li and Li Fei-Fei
Lecture Notes in Computer Science - 2010 via Local CrossRef
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