DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations

• URL: http://arxiv.org/abs/2502.06029v1
• Date: Sun, 09 Feb 2025 21:05:11 GMT
• Title: DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations
• Authors: Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb, Bruno Ribeiro, Chaim Baskin, Moshe Eliasof,
• Abstract summary: We introduce DiTASK, a novel approach to efficiently adapt pre-trained Vision Transformers for multiple tasks. Our theoretical analysis shows that DiTASK achieves full-rank updates during optimization, preserving the geometric structure of pre-trained features. Our experiments on PASCAL MTL and NYUD show that DiTASK achieves state-of-the-art performance across four dense prediction tasks, using 75% fewer parameters than existing methods.
• Score: 21.06471370479668
• License:
• Abstract: Pre-trained Vision Transformers now serve as powerful tools for computer vision. Yet, efficiently adapting them for multiple tasks remains a challenge that arises from the need to modify the rich hidden representations encoded by the learned weight matrices, without inducing interference between tasks. Current parameter-efficient methods like LoRA, which apply low-rank updates, force tasks to compete within constrained subspaces, ultimately degrading performance. We introduce DiTASK a novel Diffeomorphic Multi-Task Fine-Tuning approach that maintains pre-trained representations by preserving weight matrix singular vectors, while enabling task-specific adaptations through neural diffeomorphic transformations of the singular values. By following this approach, DiTASK enables both shared and task-specific feature modulations with minimal added parameters. Our theoretical analysis shows that DITASK achieves full-rank updates during optimization, preserving the geometric structure of pre-trained features, and establishing a new paradigm for efficient multi-task learning (MTL). Our experiments on PASCAL MTL and NYUD show that DiTASK achieves state-of-the-art performance across four dense prediction tasks, using 75% fewer parameters than existing methods.

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