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pro vyhledávání: '"Matthew Gwilliam"'
For the task of image classification, researchers work arduously to develop the next state-of-the-art (SOTA) model, each bench-marking their own performance against that of their predecessors and of their peers. Unfortunately, the metric used most fr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bba9529c8b55c5fc38a7ca3feff18ba2
http://arxiv.org/abs/2109.03156
http://arxiv.org/abs/2109.03156
Many existing works have made great strides towards reducing racial bias in face recognition. However, most of these methods attempt to rectify bias that manifests in models during training instead of directly addressing a major source of the bias, t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4ed800058c47ec440ea7e8972154779a
http://arxiv.org/abs/2109.03229
http://arxiv.org/abs/2109.03229
Publikováno v:
EACL
EACL 2021-16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, 2203-2213
STARTPAGE=2203;ENDPAGE=2213;TITLE=EACL 2021-16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
EACL 2021-16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, 2203-2213
STARTPAGE=2203;ENDPAGE=2213;TITLE=EACL 2021-16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference
Recent studies in the field of Machine Translation (MT) and Natural Language Processing (NLP) have shown that existing models amplify biases observed in the training data. The amplification of biases in language technology has mainly been examined wi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8ba05dc8cccd8b0defaeaa969793587d
https://doi.org/10.18653/v1/2021.eacl-main.188
https://doi.org/10.18653/v1/2021.eacl-main.188
Autor:
Matthew Gwilliam, Ryan Farrell
Publikováno v:
WACV
Key recognition tasks such as fine-grained visual categorization (FGVC) have benefited from increasing attention among computer vision researchers. The development and evaluation of new approaches relies heavily on benchmark datasets; such datasets a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::10c83fb9a55b8ae40de8bc435a1f3b53
https://doi.org/10.1109/wacv45572.2020.9093292
https://doi.org/10.1109/wacv45572.2020.9093292