Entropic Open-Set Active Learning Article Swipe
Bardia Safaei
,
Vibashan VS
,
Celso M. de Melo
,
Vishal M. Patel
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v38i5.28269
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v38i5.28269
Active Learning (AL) aims to enhance the performance of deep models by selecting the most informative samples for annotation from a pool of unlabeled data. Despite impressive performance in closed-set settings, most AL methods fail in real-world scenarios where the unlabeled data contains unknown categories. Recently, a few studies have attempted to tackle the AL problem for the open-set setting. However, these methods focus more on selecting known samples and do not efficiently utilize unknown samples obtained during AL rounds. In this work, we propose an Entropic Open-set AL (EOAL) framework which leverages both known and unknown distributions effectively to select informative samples during AL rounds. Specifically, our approach employs two different entropy scores. One measures the uncertainty of a sample with respect to the known-class distributions. The other measures the uncertainty of the sample with respect to the unknown-class distributions. By utilizing these two entropy scores we effectively separate the known and unknown samples from the unlabeled data resulting in better sampling. Through extensive experiments, we show that the proposed method outperforms existing state-of-the-art methods on CIFAR-10, CIFAR-100, and TinyImageNet datasets. Code is available at https://github.com/bardisafa/EOAL.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v38i5.28269
- https://ojs.aaai.org/index.php/AAAI/article/download/28269/28529
- OA Status
- diamond
- Cited By
- 15
- References
- 74
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393159168
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4393159168Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1609/aaai.v38i5.28269Digital Object Identifier
- Title
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Entropic Open-Set Active LearningWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-03-24Full publication date if available
- Authors
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Bardia Safaei, Vibashan VS, Celso M. de Melo, Vishal M. PatelList of authors in order
- Landing page
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https://doi.org/10.1609/aaai.v38i5.28269Publisher landing page
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https://ojs.aaai.org/index.php/AAAI/article/download/28269/28529Direct link to full text PDF
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YesWhether a free full text is available
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-
diamondOpen access status per OpenAlex
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https://ojs.aaai.org/index.php/AAAI/article/download/28269/28529Direct OA link when available
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Active learning (machine learning), Set (abstract data type), Computer science, Artificial intelligence, Programming languageTop concepts (fields/topics) attached by OpenAlex
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15Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 8Per-year citation counts (last 5 years)
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74Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.respect | 122, 136 |
| abstract_inverted_index.rounds. | 79, 105 |
| abstract_inverted_index.samples | 16, 68, 75, 102, 154 |
| abstract_inverted_index.scores. | 113 |
| abstract_inverted_index.studies | 48 |
| abstract_inverted_index.unknown | 43, 74, 96, 153 |
| abstract_inverted_index.utilize | 73 |
| abstract_inverted_index.Entropic | 86 |
| abstract_inverted_index.However, | 60 |
| abstract_inverted_index.Learning | 1 |
| abstract_inverted_index.Open-set | 87 |
| abstract_inverted_index.approach | 108 |
| abstract_inverted_index.contains | 42 |
| abstract_inverted_index.existing | 173 |
| abstract_inverted_index.measures | 115, 129 |
| abstract_inverted_index.obtained | 76 |
| abstract_inverted_index.open-set | 58 |
| abstract_inverted_index.proposed | 170 |
| abstract_inverted_index.separate | 149 |
| abstract_inverted_index.setting. | 59 |
| abstract_inverted_index.CIFAR-10, | 177 |
| abstract_inverted_index.Recently, | 45 |
| abstract_inverted_index.attempted | 50 |
| abstract_inverted_index.available | 184 |
| abstract_inverted_index.datasets. | 181 |
| abstract_inverted_index.different | 111 |
| abstract_inverted_index.extensive | 164 |
| abstract_inverted_index.framework | 90 |
| abstract_inverted_index.leverages | 92 |
| abstract_inverted_index.resulting | 159 |
| abstract_inverted_index.sampling. | 162 |
| abstract_inverted_index.scenarios | 37 |
| abstract_inverted_index.selecting | 12, 66 |
| abstract_inverted_index.settings, | 30 |
| abstract_inverted_index.unlabeled | 23, 40, 157 |
| abstract_inverted_index.utilizing | 142 |
| abstract_inverted_index.CIFAR-100, | 178 |
| abstract_inverted_index.annotation | 18 |
| abstract_inverted_index.closed-set | 29 |
| abstract_inverted_index.impressive | 26 |
| abstract_inverted_index.real-world | 36 |
| abstract_inverted_index.categories. | 44 |
| abstract_inverted_index.effectively | 98, 148 |
| abstract_inverted_index.efficiently | 72 |
| abstract_inverted_index.informative | 15, 101 |
| abstract_inverted_index.known-class | 125 |
| abstract_inverted_index.outperforms | 172 |
| abstract_inverted_index.performance | 7, 27 |
| abstract_inverted_index.uncertainty | 117, 131 |
| abstract_inverted_index.TinyImageNet | 180 |
| abstract_inverted_index.experiments, | 165 |
| abstract_inverted_index.Specifically, | 106 |
| abstract_inverted_index.distributions | 97 |
| abstract_inverted_index.unknown-class | 139 |
| abstract_inverted_index.distributions. | 126, 140 |
| abstract_inverted_index.state-of-the-art | 174 |
| abstract_inverted_index.https://github.com/bardisafa/EOAL. | 186 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.92619926 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |