Soft-TransFormers for Continual Learning
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914511555919872 |
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| author | Kang, Haeyong Yoo, Chang D. |
| author_facet | Kang, Haeyong Yoo, Chang D. |
| contents | Inspired by the \emph{Well-initialized Lottery Ticket Hypothesis (WLTH)}, we introduce Soft-Transformer (Soft-TF), a parameter-efficient framework for continual learning that leverages soft, real-valued subnetworks over a frozen pre-trained Transformer. Instead of relying on manually designed prompts or adapters, Soft-TF learns task-specific multiplicative masks applied to the key, query, value, and output projections in self-attention. These masks enable smooth and stable task adaptation while preserving shared representations. Combined with a lightweight dual-prompt mechanism, Soft-TF maintains strong knowledge retention and mitigates Catastrophic Forgetting (CF). Across multiple continual learning benchmarks, Soft-TF achieves state-of-the-art performance, consistently outperforming prompt-based, adapter-based, and LoRA-style baselines while requiring minimal additional parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16073 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Soft-TransFormers for Continual Learning Kang, Haeyong Yoo, Chang D. Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Inspired by the \emph{Well-initialized Lottery Ticket Hypothesis (WLTH)}, we introduce Soft-Transformer (Soft-TF), a parameter-efficient framework for continual learning that leverages soft, real-valued subnetworks over a frozen pre-trained Transformer. Instead of relying on manually designed prompts or adapters, Soft-TF learns task-specific multiplicative masks applied to the key, query, value, and output projections in self-attention. These masks enable smooth and stable task adaptation while preserving shared representations. Combined with a lightweight dual-prompt mechanism, Soft-TF maintains strong knowledge retention and mitigates Catastrophic Forgetting (CF). Across multiple continual learning benchmarks, Soft-TF achieves state-of-the-art performance, consistently outperforming prompt-based, adapter-based, and LoRA-style baselines while requiring minimal additional parameters. |
| title | Soft-TransFormers for Continual Learning |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.16073 |