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Multimodal meta-learning through meta-learned task representations
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0003-0185-5038
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-2859-6155
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0001-5163-2997
2024 (English)In: Neural Computing & Applications, ISSN 0941-0643, E-ISSN 1433-3058Article in journal (Refereed) Epub ahead of print
Abstract [en]

Few-shot meta-learning involves training a model on multiple tasks to enable it to efficiently adapt to new, previously unseen tasks with only a limited number of samples. However, current meta-learning methods assume that all tasks are closely related and belong to a common domain, whereas in practice, tasks can be highly diverse and originate from multiple domains, resulting in a multimodal task distribution. This poses a challenge for existing methods as they struggle to learn a shared representation that can be easily adapted to all tasks within the distribution. To address this challenge, we propose a meta-learning framework that can handle multimodal task distributions by conditioning the model on the current task, resulting in a faster adaptation. Our proposed method learns to encode each task and generate task embeddings that modulate the model’s activations. The resulting modulated model becomes specialized for the current task and leads to more effective adaptation. Our framework is designed to work in a realistic setting where the mode from which a task is sampled is unknown. Nonetheless, we also explore the possibility of incorporating auxiliary information, such as the task-mode-label, to further enhance the performance of our method if such information is available. We evaluate our proposed framework on various few-shot regression and image classification tasks, demonstrating its superiority over other state-of-the-art meta-learning methods. The results highlight the benefits of learning to embed task-specific information in the model to guide the adaptation when tasks are sampled from a multimodal distribution. © The Author(s) 2024.

Place, publisher, year, edition, pages
London: Springer, 2024.
Keywords [en]
Meta-learning, Few-shot learning, Transfer learning, Task representation, Multimodal distribution
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-52924DOI: 10.1007/s00521-024-09540-1ISI: 001167510900006Scopus ID: 2-s2.0-85187241643OAI: oai:DiVA.org:hh-52924DiVA, id: diva2:1846103
Funder
Halmstad UniversityKnowledge FoundationAvailable from: 2024-03-21 Created: 2024-03-21 Last updated: 2024-03-28Bibliographically approved

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paper(952 kB)332 downloads
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Vettoruzzo, AnnaBouguelia, Mohamed-RafikRögnvaldsson, Thorsteinn

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CiteExportLink to record
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