Academic Journal
Contrastive learning of generalized game representations
| Τίτλος: | Contrastive learning of generalized game representations |
|---|---|
| Συγγραφείς: | Trivedi, Chintan, Liapis, Antonios, Yannakakis, Georgios N. |
| Στοιχεία εκδότη: | arXiv |
| Έτος έκδοσης: | 2021 |
| Συλλογή: | University of Malta: OAR@UM / L-Università ta' Malta |
| Θεματικοί όροι: | Games -- Design, Computer games -- Programming, Video games -- Design, Motion -- Computer simulation, Interactive multimedia |
| Περιγραφή: | Representing games through their pixels offers a promising approach for building general-purpose and versatile game models. While games are not merely images, neural network models trained on game pixels often capture differences of the visual style of the image rather than the content of the game. As a result, such models cannot generalize well even within similar games of the same genre. In this paper we build on recent advances in contrastive learning and showcase its benefits for representation learning in games. Learning to contrast images of games not only classifies games in a more efficient manner; it also yields models that separate games in a more meaningful fashion by ignoring the visual style and focusing, instead, on their content. Our results in a large dataset of sports video games containing 100k images across 175 games and 10 game genres suggest that contrastive learning is better suited for learning generalized game representations compared to conventional supervised learning. The findings of this study bring us closer to universal visual encoders for games that can be reused across previously unseen games without requiring retraining or fine-tuning. ; N/A |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | English |
| Relation: | Trivedi, C., Liapis, A., & Yannakakis, G. N. (2021). Contrastive learning of generalized game representations. arXiv preprint arXiv:2106.10060.; https://www.um.edu.mt/library/oar/handle/123456789/80762 |
| Διαθεσιμότητα: | https://www.um.edu.mt/library/oar/handle/123456789/80762 |
| Rights: | info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. |
| Αριθμός Καταχώρησης: | edsbas.1F9850AB |
| Βάση Δεδομένων: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://www.um.edu.mt/library/oar/handle/123456789/80762# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: Contrastive learning of generalized game representations – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Trivedi%2C+Chintan%22">Trivedi, Chintan</searchLink><br /><searchLink fieldCode="AR" term="%22Liapis%2C+Antonios%22">Liapis, Antonios</searchLink><br /><searchLink fieldCode="AR" term="%22Yannakakis%2C+Georgios+N%2E%22">Yannakakis, Georgios N.</searchLink> – Name: Publisher Label: Publisher Information Group: PubInfo Data: arXiv – Name: DatePubCY Label: Publication Year Group: Date Data: 2021 – Name: Subset Label: Collection Group: HoldingsInfo Data: University of Malta: OAR@UM / L-Università ta' Malta – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Games+--+Design%22">Games -- Design</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+games+--+Programming%22">Computer games -- Programming</searchLink><br /><searchLink fieldCode="DE" term="%22Video+games+--+Design%22">Video games -- Design</searchLink><br /><searchLink fieldCode="DE" term="%22Motion+--+Computer+simulation%22">Motion -- Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Interactive+multimedia%22">Interactive multimedia</searchLink> – Name: Abstract Label: Description Group: Ab Data: Representing games through their pixels offers a promising approach for building general-purpose and versatile game models. While games are not merely images, neural network models trained on game pixels often capture differences of the visual style of the image rather than the content of the game. As a result, such models cannot generalize well even within similar games of the same genre. In this paper we build on recent advances in contrastive learning and showcase its benefits for representation learning in games. Learning to contrast images of games not only classifies games in a more efficient manner; it also yields models that separate games in a more meaningful fashion by ignoring the visual style and focusing, instead, on their content. Our results in a large dataset of sports video games containing 100k images across 175 games and 10 game genres suggest that contrastive learning is better suited for learning generalized game representations compared to conventional supervised learning. The findings of this study bring us closer to universal visual encoders for games that can be reused across previously unseen games without requiring retraining or fine-tuning. ; N/A – Name: TypeDocument Label: Document Type Group: TypDoc Data: article in journal/newspaper – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: Trivedi, C., Liapis, A., & Yannakakis, G. N. (2021). Contrastive learning of generalized game representations. arXiv preprint arXiv:2106.10060.; https://www.um.edu.mt/library/oar/handle/123456789/80762 – Name: URL Label: Availability Group: URL Data: https://www.um.edu.mt/library/oar/handle/123456789/80762 – Name: Copyright Label: Rights Group: Cpyrght Data: info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. – Name: AN Label: Accession Number Group: ID Data: edsbas.1F9850AB |
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| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: Games -- Design Type: general – SubjectFull: Computer games -- Programming Type: general – SubjectFull: Video games -- Design Type: general – SubjectFull: Motion -- Computer simulation Type: general – SubjectFull: Interactive multimedia Type: general Titles: – TitleFull: Contrastive learning of generalized game representations Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Trivedi, Chintan – PersonEntity: Name: NameFull: Liapis, Antonios – PersonEntity: Name: NameFull: Yannakakis, Georgios N. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2021 Identifiers: – Type: issn-locals Value: edsbas – Type: issn-locals Value: edsbas.oa |
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