Wordnet for ImageNet Challenge: Difference between revisions
From Algolit
(Created page with "by Algolit Wordnet, created in 1985, is a hierarchical taxonomy that describes the world. It was inspired by theories of human semantic memory developed in the late 1960s. No...") |
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by Algolit | by Algolit | ||
| − | + | Created in 1985, [https://wordnet.princeton.edu/ Wordnet] is a hierarchical taxonomy that describes the world. It was inspired by theories of human semantic memory developed in the late 1960s. Nouns, verbs, adjectives and adverbs are grouped into synonyms sets or synsets, expressing a different concept. | |
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| − | + | ImageNet is an image dataset based on the WordNet 3.0 nouns hierarchy. Each synset is depicted by thousands of images. From 2010 until 2017, the [http://image-net.org/challenges/LSVRC/ ImageNet Large Scale Visual Recognition Challenge (ILSVRC)] was a key benchmark in object category classification for pictures, having a major impact on software for photography, image searches, image recognition. | |
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| + | 1000 synsets (Vinyl Edition) contains the 1000 synsets used in this challenge recorded in the highest sound quality that this analog format allows. This work highlights the importance of the datasets used to train artificial intelligence (AI) models that run on devices we use on a daily basis. Some of them inherit classifications that were conceived more than 30 years ago. This sound work is an invitation to thoughtfully analyse them. | ||
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| + | ---------------------------------------------------- | ||
Concept & recording: Javier Lloret | Concept & recording: Javier Lloret | ||
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| + | Voices: Sara Hamadeh & Joseph Hughes | ||
Latest revision as of 08:54, 22 March 2019
by Algolit
Created in 1985, Wordnet is a hierarchical taxonomy that describes the world. It was inspired by theories of human semantic memory developed in the late 1960s. Nouns, verbs, adjectives and adverbs are grouped into synonyms sets or synsets, expressing a different concept.
ImageNet is an image dataset based on the WordNet 3.0 nouns hierarchy. Each synset is depicted by thousands of images. From 2010 until 2017, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) was a key benchmark in object category classification for pictures, having a major impact on software for photography, image searches, image recognition.
1000 synsets (Vinyl Edition) contains the 1000 synsets used in this challenge recorded in the highest sound quality that this analog format allows. This work highlights the importance of the datasets used to train artificial intelligence (AI) models that run on devices we use on a daily basis. Some of them inherit classifications that were conceived more than 30 years ago. This sound work is an invitation to thoughtfully analyse them.
Concept & recording: Javier Lloret
Voices: Sara Hamadeh & Joseph Hughes