Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning

• URL: http://arxiv.org/abs/2303.08566v2
• Date: Thu, 31 Aug 2023 08:17:57 GMT
• Title: Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning
• Authors: Haoyu He, Jianfei Cai, Jing Zhang, Dacheng Tao, Bohan Zhuang
• Abstract summary: We propose a novel visual. sensuous-aware fine-Tuning (SPT) scheme. SPT allocates trainable parameters to task-specific important positions. Experiments on a wide range of downstream recognition tasks show that our SPT is complementary to the existing PEFT methods.
• Score: 91.5113227694443
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual Parameter-Efficient Fine-Tuning (PEFT) has become a powerful alternative for full fine-tuning so as to adapt pre-trained vision models to downstream tasks, which only tunes a small number of parameters while freezing the vast majority ones to ease storage burden and optimization difficulty. However, existing PEFT methods introduce trainable parameters to the same positions across different tasks depending solely on human heuristics and neglect the domain gaps. To this end, we study where to introduce and how to allocate trainable parameters by proposing a novel Sensitivity-aware visual Parameter-efficient fine-Tuning (SPT) scheme, which adaptively allocates trainable parameters to task-specific important positions given a desired tunable parameter budget. Specifically, our SPT first quickly identifies the sensitive parameters that require tuning for a given task in a data-dependent way. Next, our SPT further boosts the representational capability for the weight matrices whose number of sensitive parameters exceeds a pre-defined threshold by utilizing existing structured tuning methods, e.g., LoRA [23] or Adapter [22], to replace directly tuning the selected sensitive parameters (unstructured tuning) under the budget. Extensive experiments on a wide range of downstream recognition tasks show that our SPT is complementary to the existing PEFT methods and largely boosts their performance, e.g., SPT improves Adapter with supervised pre-trained ViT-B/16 backbone by 4.2% and 1.4% mean Top-1 accuracy, reaching SOTA performance on FGVC and VTAB-1k benchmarks, respectively. Source code is at https://github.com/ziplab/SPT

Related papers

• Pre-training Everywhere: Parameter-Efficient Fine-Tuning for Medical Image Analysis via Target Parameter Pre-training [17.433808197776003]
  We propose a simple yet effective fine-tuning framework based on Target Pre-training (TPP) TPP includes an additional stage before PEFT to pre-train these target parameters. TPP can be easily integrated into existing PEFT methods, significantly improving performance.
  arXiv  Detail & Related papers  (2024-08-27T12:48:46Z)
• Scaling Exponents Across Parameterizations and Optimizers [94.54718325264218]
  We propose a new perspective on parameterization by investigating a key assumption in prior work. Our empirical investigation includes tens of thousands of models trained with all combinations of threes. We find that the best learning rate scaling prescription would often have been excluded by the assumptions in prior work.
  arXiv  Detail & Related papers  (2024-07-08T12:32:51Z)
• ETHER: Efficient Finetuning of Large-Scale Models with Hyperplane Reflections [59.839926875976225]
  We propose the ETHER transformation family, which performs Efficient fineTuning via HypErplane Reflections. In particular, we introduce ETHER and its relaxation ETHER+, which match or outperform existing PEFT methods with significantly fewer parameters.
  arXiv  Detail & Related papers  (2024-05-30T17:26:02Z)
• Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation [67.13876021157887]
  Dynamic Tuning (DyT) is a novel approach to improve both parameter and inference efficiency for ViT adaptation. DyT achieves superior performance compared to existing PEFT methods while evoking only 71% of their FLOPs on the VTAB-1K benchmark.
  arXiv  Detail & Related papers  (2024-03-18T14:05:52Z)
• Dynamic Adapter Meets Prompt Tuning: Parameter-Efficient Transfer Learning for Point Cloud Analysis [51.14136878142034]
  Point cloud analysis has achieved outstanding performance by transferring point cloud pre-trained models. Existing methods for model adaptation usually update all model parameters, which is inefficient as it relies on high computational costs. In this paper, we aim to study parameter-efficient transfer learning for point cloud analysis with an ideal trade-off between task performance and parameter efficiency.
  arXiv  Detail & Related papers  (2024-03-03T08:25:04Z)
• Parameter-Efficient Fine-Tuning without Introducing New Latency [7.631596468553607]
  We introduce a novel adapter technique that directly applies the adapter to pre-trained parameters instead of the hidden representation. Our proposed method attains a new state-of-the-art outcome in terms of both performance and storage efficiency, storing only 0.03% parameters of full fine-tuning.
  arXiv  Detail & Related papers  (2023-05-26T08:44:42Z)
• Evaluating Parameter-Efficient Transfer Learning Approaches on SURE Benchmark for Speech Understanding [40.27182770995891]
  Fine-tuning is widely used as the default algorithm for transfer learning from pre-trained models. We introduce the Speech UndeRstanding Evaluation (SURE) benchmark for parameter-efficient learning for various speech-processing tasks.
  arXiv  Detail & Related papers  (2023-03-02T08:57:33Z)
• Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning [126.84770886628833]
  Existing finetuning methods either tune all parameters of the pretrained model (full finetuning) or only tune the last linear layer (linear probing) We propose a new parameter-efficient finetuning method termed as SSF, representing that researchers only need to Scale and Shift the deep Features extracted by a pre-trained model to catch up with the performance full finetuning.
  arXiv  Detail & Related papers  (2022-10-17T08:14:49Z)
• Visual Prompt Tuning for Test-time Domain Adaptation [48.16620171809511]
  We propose a simple recipe called data-efficient prompt tuning (DePT) with two key ingredients. We find such parameter-efficient finetuning can efficiently adapt the model representation to the target domain without overfitting to the noise in the learning objective. With much fewer parameters, DePT demonstrates not only state-of-the-art performance on major adaptation benchmarks, but also superior data efficiency.
  arXiv  Detail & Related papers  (2022-10-10T16:45:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.