Certified PEFTSmoothing: Efficient Conversion of Base Models into Certifiably Robust Classifiers

Основные понятия

PEFTSmoothing leverages Parameter-Efficient Fine-Tuning (PEFT) methods to efficiently guide large-scale vision models like ViT to learn the noise-augmented data distribution, enabling the conversion of base models into certifiably robust classifiers.

Аннотация

The content discusses the development of PEFTSmoothing, a method to efficiently convert base models into certifiably robust classifiers using Parameter-Efficient Fine-Tuning (PEFT) techniques.

Key highlights:

• Randomized smoothing is the state-of-the-art certified robustness method, but it requires retraining the base model from scratch, which is computationally expensive for large-scale models.
• Denoised smoothing, which uses a custom-trained denoiser, introduces additional complexity and sub-optimal certified performance.
• PEFTSmoothing leverages PEFT methods like Prompt-tuning, LoRA, and Adapter to guide the base model to learn the noise-augmented data distribution, achieving high certified accuracy while significantly reducing the computational cost.
• PEFTSmoothing outperforms denoised smoothing on CIFAR-10 and achieves comparable performance to diffusion-based denoising on ImageNet.
• The authors also propose black-box PEFTSmoothing to handle cases where the base model is not accessible, and demonstrate the possibility of integrating PEFTSmoothing with PEFT for downstream dataset adaptation.

Статистика

ViT-B/16 pre-trained on ImageNet-21k and fine-tuned on CIFAR-10 achieves over 98% certified accuracy at σ = 0.25, 20% higher than state-of-the-art denoised smoothing.
ViT-B/16 pre-trained on ImageNet and fine-tuned on ImageNet2012 achieves over 61% certified accuracy at σ = 0.5, 30% higher than CNN-based denoiser and comparable to diffusion-based denoiser.
PEFTSmoothing reduces the training parameters by 1000 times compared to diffusion-based denoisers and 10 times compared to CNN-based denoisers.

Цитаты

"PEFTSmoothing leverages Parameter-Efficient Fine-Tuning (PEFT) methods to efficiently guide large-scale vision models like ViT to learn the noise-augmented data distribution, enabling the conversion of base models into certifiably robust classifiers."
"PEFTSmoothing outperforms denoised smoothing on CIFAR-10 and achieves comparable performance to diffusion-based denoising on ImageNet, while significantly reducing the computational cost."

Ключевые выводы из

Certified PEFTSmoothing

by Chengyan Fu,... в arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05350.pdf

Дополнительные вопросы

How can PEFTSmoothing be extended to other types of neural networks beyond vision models, such as language models or multimodal models

PEFTSmoothing can be extended to other types of neural networks beyond vision models by adapting the fine-tuning process to suit the specific architecture and requirements of different models. For language models, such as transformers used in natural language processing tasks, PEFT methods like Prompt-tuning can be applied to adjust the model's parameters efficiently for downstream tasks. By incorporating soft prompts or adapting the attention mechanisms, the language model can be fine-tuned to learn noise-augmented data distributions. Similarly, for multimodal models that combine vision and language modalities, a hybrid approach can be used to fine-tune the model for robustness while adapting it to handle diverse data types effectively.

What are the potential limitations or drawbacks of the PEFT-based approach compared to other certified robustness methods, and how can they be addressed

One potential limitation of the PEFT-based approach compared to other certified robustness methods is the trade-off between efficiency and accuracy. While PEFTSmoothing offers parameter-efficient fine-tuning and achieves high certified accuracy, it may not always outperform more complex denoising methods in terms of robustness. To address this, a hybrid approach that combines PEFTSmoothing with advanced denoising techniques could be explored. This hybrid model could leverage the efficiency of PEFT methods while incorporating the denoising capabilities of more sophisticated architectures to enhance robustness further.
Another drawback could be the generalizability of PEFTSmoothing across different types of models and datasets. To mitigate this, extensive experimentation and optimization of hyperparameters for specific models and tasks may be necessary. Additionally, ongoing research and development in PEFT methods could lead to enhancements that address these limitations and improve the overall performance of PEFTSmoothing.

Given the success of PEFTSmoothing in achieving both certified robustness and downstream dataset adaptation, how can this approach be leveraged to enable efficient and robust fine-tuning of large-scale models for a wide range of applications

Given the success of PEFTSmoothing in achieving both certified robustness and downstream dataset adaptation, this approach can be leveraged to enable efficient and robust fine-tuning of large-scale models for a wide range of applications by incorporating domain-specific knowledge and task-specific requirements. By tailoring the fine-tuning process to the unique characteristics of different datasets and tasks, PEFTSmoothing can optimize the model's performance while ensuring robustness to adversarial perturbations.
Furthermore, the integration of PEFTSmoothing with transfer learning techniques can facilitate the adaptation of pre-trained models to new tasks and domains, enhancing the model's versatility and applicability. By fine-tuning the model with PEFT methods, researchers and practitioners can achieve a balance between efficiency, accuracy, and robustness, making large-scale model training more accessible and effective for various applications in machine learning and artificial intelligence.