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DePT: Decoupled Prompt Tuning (2309.07439v2)

Published 14 Sep 2023 in cs.CV

Abstract: This work breaks through the Base-New Tradeoff (BNT)dilemma in prompt tuning, i.e., the better the tuned model generalizes to the base (or target) task, the worse it generalizes to new tasks, and vice versa. Specifically, through an in-depth analysis of the learned features of the base and new tasks, we observe that the BNT stems from a channel bias issue, i.e., the vast majority of feature channels are occupied by base-specific knowledge, resulting in the collapse of taskshared knowledge important to new tasks. To address this, we propose the Decoupled Prompt Tuning (DePT) framework, which decouples base-specific knowledge from feature channels into an isolated feature space during prompt tuning, so as to maximally preserve task-shared knowledge in the original feature space for achieving better zero-shot generalization on new tasks. Importantly, our DePT is orthogonal to existing prompt tuning methods, hence it can improve all of them. Extensive experiments on 11 datasets show the strong flexibility and effectiveness of DePT. Our code and pretrained models are available at https://github.com/Koorye/DePT.

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Citations (13)
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Summary

  • The paper identifies channel bias as the root cause of the Base-New Tradeoff by showing how base-specific dominance impairs task-shared generalization.
  • It introduces the Decoupled Prompt Tuning (DePT) framework featuring a dual-head design to segregate base-specific and shared knowledge.
  • Empirical results across 11 datasets demonstrate consistent gains, with improvements of 1.31%-3.17% on base tasks and 0.71%-2.23% on new tasks.

Decoupled Prompt Tuning: A Comprehensive Analysis

The paper "DePT: Decoupled Prompt Tuning" presents an innovative framework designed to overcome the Base-New Tradeoff (BNT) dilemma in prompt tuning within the context of vision-language pre-trained models (VLPMs). The BNT dilemma highlights a significant challenge in prompt tuning, where improved generalization to base tasks often results in reduced performance on new tasks, and vice versa. Through an extensive analysis, this paper identifies the underlying cause of the BNT as a channel bias issue. The authors propose a novel Decoupled Prompt Tuning (DePT) framework that aims to resolve this problem by decoupling base-specific knowledge and comprehensive task-shared knowledge in a manner that preserves the zero-shot generalization capabilities of VLPMs.

Key Contributions

  1. Channel Bias Identification: The paper reveals that the BNT dilemma can be attributed to a channel bias issue in which base-specific knowledge dominates most feature channels during prompt tuning, leading to the collapse of task-shared knowledge needed for new tasks. This realization provides a new perspective on understanding the underlying mechanisms of performance degradation across unseen tasks.
  2. Decoupled Prompt Tuning (DePT) Framework: DePT introduces a Channel Adjusted Transfer (CAT) head during prompt tuning, which functions by isolating the base-specific knowledge into a separate feature space. This isolation is strategically implemented to allow the original feature space to retain task-shared knowledge critical for the generalization to new tasks. By using dual heads—one for base-specific and one for task-shared knowledge—DePT maximizes zero-shot generalization.
  3. Orthogonality to Existing Methods: The proposed framework is orthogonal to existing prompt tuning approaches, allowing it to enhance them with minimal computational overhead. This compatibility suggests that DePT can be seamlessly integrated into various current methodologies without necessitating extensive system overhauls.
  4. Empirical Validation Across Datasets: Extensive experiments were conducted using 11 diverse datasets, demonstrating that DePT consistently improves the performance of existing prompt tuning methods. Notably, DePT achieved significant gains without performance tradeoffs between base and new tasks.
  5. Numerical Results: DePT delivers consistent improvements with absolute gains ranging from 1.31% to 3.17% on base tasks and 0.71% to 2.23% on new tasks, averaged across the datasets. These results confirm the robustness and efficacy of DePT in addressing the BNT problem.

Implications and Future Directions

The research presented in this paper has significant practical and theoretical implications. By successfully tackling the BNT dilemma, DePT enhances the adaptability of vision-LLMs to diverse and unseen datasets, thus expanding their applicability across a variety of real-world scenarios. Furthermore, exploring the decoupling of feature spaces may stimulate further research in prompt tuning and vision-language pretraining, leading to even more sophisticated and efficient tuning methods.

Future work may focus on extending the DePT framework to additional model architectures beyond the VLPMs, including architectures in fields like natural language processing and multi-modal systems beyond visual and linguistic inputs. Additionally, investigating the potential of DePT in conjunction with other parameter-efficient learning paradigms, such as adapter tuning methods, could unlock further advancements in model adaptation strategies.

In conclusion, the Decoupled Prompt Tuning framework represents a valuable advancement in addressing one of the core challenges in adapting pre-trained models to new tasks, providing a pathway for enhanced flexibility and generalization in a rapidly evolving AI landscape.

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  1. GitHub - Koorye/DePT: [CVPR 2024] Offical implemention of the paper "DePT: Decoupled Prompt Tuning" (107 stars)

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