Prompting-based Temporal Domain Generalization

• URL: http://arxiv.org/abs/2310.02473v2
• Date: Thu, 15 Feb 2024 19:22:06 GMT
• Title: Prompting-based Temporal Domain Generalization
• Authors: Sepidehsadat Hosseini, Mengyao Zhai, Hossein Hajimirsadegh, Frederick Tung
• Abstract summary: This paper presents a novel prompting-based approach to temporal domain generalization. Our method adapts a trained model to temporal drift by learning global prompts, domain-specific prompts, and drift-aware prompts. Experiments on classification, regression, and time series forecasting tasks demonstrate the generality of the proposed approach.
• Score: 10.377683220196873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning traditionally assumes that the training and testing data are distributed independently and identically. However, in many real-world settings, the data distribution can shift over time, leading to poor generalization of trained models in future time periods. This paper presents a novel prompting-based approach to temporal domain generalization that is parameter-efficient, time-efficient, and does not require access to future data during training. Our method adapts a trained model to temporal drift by learning global prompts, domain-specific prompts, and drift-aware prompts that capture underlying temporal dynamics. Experiments on classification, regression, and time series forecasting tasks demonstrate the generality of the proposed approach. The code repository will be publicly shared.

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