Exploring new ways: Enforcing representational dissimilarity to learn new features and reduce error consistency Article Swipe
Tassilo Wald
,
Constantin Ulrich
,
Fabian Isensee
,
David Zimmerer
,
Gregor Koehler
,
Michael Baumgartner
,
Klaus Maier‐Hein
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.02516
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.02516
Independently trained machine learning models tend to learn similar features. Given an ensemble of independently trained models, this results in correlated predictions and common failure modes. Previous attempts focusing on decorrelation of output predictions or logits yielded mixed results, particularly due to their reduction in model accuracy caused by conflicting optimization objectives. In this paper, we propose the novel idea of utilizing methods of the representational similarity field to promote dissimilarity during training instead of measuring similarity of trained models. To this end, we promote intermediate representations to be dissimilar at different depths between architectures, with the goal of learning robust ensembles with disjoint failure modes. We show that highly dissimilar intermediate representations result in less correlated output predictions and slightly lower error consistency, resulting in higher ensemble accuracy. With this, we shine first light on the connection between intermediate representations and their impact on the output predictions.
Related Topics
Concepts
Consistency (knowledge bases)
Similarity (geometry)
Disjoint sets
Computer science
Artificial intelligence
Decorrelation
Machine learning
Field (mathematics)
Mean squared prediction error
Connection (principal bundle)
Ensemble learning
Mathematics
Algorithm
Combinatorics
Pure mathematics
Geometry
Image (mathematics)
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.02516
- https://arxiv.org/pdf/2307.02516
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4383604335
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4383604335Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2307.02516Digital Object Identifier
- Title
-
Exploring new ways: Enforcing representational dissimilarity to learn new features and reduce error consistencyWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-05Full publication date if available
- Authors
-
Tassilo Wald, Constantin Ulrich, Fabian Isensee, David Zimmerer, Gregor Koehler, Michael Baumgartner, Klaus Maier‐HeinList of authors in order
- Landing page
-
https://arxiv.org/abs/2307.02516Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2307.02516Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2307.02516Direct OA link when available
- Concepts
-
Consistency (knowledge bases), Similarity (geometry), Disjoint sets, Computer science, Artificial intelligence, Decorrelation, Machine learning, Field (mathematics), Mean squared prediction error, Connection (principal bundle), Ensemble learning, Mathematics, Algorithm, Combinatorics, Pure mathematics, Geometry, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.trained | 1, 15, 78 |
| abstract_inverted_index.yielded | 36 |
| abstract_inverted_index.Previous | 26 |
| abstract_inverted_index.accuracy | 46 |
| abstract_inverted_index.attempts | 27 |
| abstract_inverted_index.disjoint | 103 |
| abstract_inverted_index.ensemble | 12, 127 |
| abstract_inverted_index.focusing | 28 |
| abstract_inverted_index.learning | 3, 99 |
| abstract_inverted_index.results, | 38 |
| abstract_inverted_index.slightly | 120 |
| abstract_inverted_index.training | 72 |
| abstract_inverted_index.accuracy. | 128 |
| abstract_inverted_index.different | 91 |
| abstract_inverted_index.ensembles | 101 |
| abstract_inverted_index.features. | 9 |
| abstract_inverted_index.measuring | 75 |
| abstract_inverted_index.reduction | 43 |
| abstract_inverted_index.resulting | 124 |
| abstract_inverted_index.utilizing | 61 |
| abstract_inverted_index.connection | 137 |
| abstract_inverted_index.correlated | 20, 116 |
| abstract_inverted_index.dissimilar | 89, 110 |
| abstract_inverted_index.similarity | 66, 76 |
| abstract_inverted_index.conflicting | 49 |
| abstract_inverted_index.objectives. | 51 |
| abstract_inverted_index.predictions | 21, 33, 118 |
| abstract_inverted_index.consistency, | 123 |
| abstract_inverted_index.intermediate | 85, 111, 139 |
| abstract_inverted_index.optimization | 50 |
| abstract_inverted_index.particularly | 39 |
| abstract_inverted_index.predictions. | 147 |
| abstract_inverted_index.Independently | 0 |
| abstract_inverted_index.decorrelation | 30 |
| abstract_inverted_index.dissimilarity | 70 |
| abstract_inverted_index.independently | 14 |
| abstract_inverted_index.architectures, | 94 |
| abstract_inverted_index.representations | 86, 112, 140 |
| abstract_inverted_index.representational | 65 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |