Self-Supervised Learning from Semantically Imprecise Data
Learning from imprecise labels such as "animal" or "bird", but making precise predictions like "snow bunting" at inference time is an important capability for any classifier when expertly labeled training data is scarce. Contributions by volunteers or results of web cra...
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ftdatacite:10.48550/arxiv.2104.10901 2023-05-15T18:20:04+02:00 Self-Supervised Learning from Semantically Imprecise Data Brust, Clemens-Alexander Barz, Björn Denzler, Joachim 2021 https://dx.doi.org/10.48550/arxiv.2104.10901 https://arxiv.org/abs/2104.10901 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences Article CreativeWork article Preprint 2021 ftdatacite https://doi.org/10.48550/arxiv.2104.10901 2022-03-10T14:36:28Z Learning from imprecise labels such as "animal" or "bird", but making precise predictions like "snow bunting" at inference time is an important capability for any classifier when expertly labeled training data is scarce. Contributions by volunteers or results of web crawling lack precision in this manner, but are still valuable. And crucially, these weakly labeled examples are available in larger quantities for lower cost than high-quality bespoke training data. CHILLAX, a recently proposed method to tackle this task, leverages a hierarchical classifier to learn from imprecise labels. However, it has two major limitations. First, it does not learn from examples labeled as the root of the hierarchy, e.g., "object". Second, an extrapolation of annotations to precise labels is only performed at test time, where confident extrapolations could be already used as training data. In this work, we extend CHILLAX with a self-supervised scheme using constrained semantic extrapolation to generate pseudo-labels. This addresses the second concern, which in turn solves the first problem, enabling an even weaker supervision requirement than CHILLAX. We evaluate our approach empirically, showing that our method allows for a consistent accuracy improvement of 0.84 to 1.19 percent points over CHILLAX and is suitable as a drop-in replacement without any negative consequences such as longer training times. : 9 pages. Accepted for publication at VISAPP 2022 Article in Journal/Newspaper Snow Bunting DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences |
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Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences Brust, Clemens-Alexander Barz, Björn Denzler, Joachim Self-Supervised Learning from Semantically Imprecise Data |
topic_facet |
Computer Vision and Pattern Recognition cs.CV FOS Computer and information sciences |
description |
Learning from imprecise labels such as "animal" or "bird", but making precise predictions like "snow bunting" at inference time is an important capability for any classifier when expertly labeled training data is scarce. Contributions by volunteers or results of web crawling lack precision in this manner, but are still valuable. And crucially, these weakly labeled examples are available in larger quantities for lower cost than high-quality bespoke training data. CHILLAX, a recently proposed method to tackle this task, leverages a hierarchical classifier to learn from imprecise labels. However, it has two major limitations. First, it does not learn from examples labeled as the root of the hierarchy, e.g., "object". Second, an extrapolation of annotations to precise labels is only performed at test time, where confident extrapolations could be already used as training data. In this work, we extend CHILLAX with a self-supervised scheme using constrained semantic extrapolation to generate pseudo-labels. This addresses the second concern, which in turn solves the first problem, enabling an even weaker supervision requirement than CHILLAX. We evaluate our approach empirically, showing that our method allows for a consistent accuracy improvement of 0.84 to 1.19 percent points over CHILLAX and is suitable as a drop-in replacement without any negative consequences such as longer training times. : 9 pages. Accepted for publication at VISAPP 2022 |
format |
Article in Journal/Newspaper |
author |
Brust, Clemens-Alexander Barz, Björn Denzler, Joachim |
author_facet |
Brust, Clemens-Alexander Barz, Björn Denzler, Joachim |
author_sort |
Brust, Clemens-Alexander |
title |
Self-Supervised Learning from Semantically Imprecise Data |
title_short |
Self-Supervised Learning from Semantically Imprecise Data |
title_full |
Self-Supervised Learning from Semantically Imprecise Data |
title_fullStr |
Self-Supervised Learning from Semantically Imprecise Data |
title_full_unstemmed |
Self-Supervised Learning from Semantically Imprecise Data |
title_sort |
self-supervised learning from semantically imprecise data |
publisher |
arXiv |
publishDate |
2021 |
url |
https://dx.doi.org/10.48550/arxiv.2104.10901 https://arxiv.org/abs/2104.10901 |
genre |
Snow Bunting |
genre_facet |
Snow Bunting |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.2104.10901 |
_version_ |
1766197542620495872 |