Awesome-Parameter-Efficient-Transfer-Learning: A Comprehensive Overview
Introduction
The "Awesome-Parameter-Efficient-Transfer-Learning" project serves as a remarkable repository for researchers and enthusiasts interested in parameter-efficient techniques for transfer learning, particularly in the realms of computer vision and multimodal applications. This initiative compiles various scholarly articles that focus on refining how large pre-trained models can be adapted to specific tasks with minimal parameter adjustments, thereby mitigating the prevalent issues of overfitting and resource constraints associated with large models.
Why Parameter Efficient?
In the traditional deep learning paradigm, models are pre-trained on massive datasets and then fully fine-tuned for specific downstream tasks. However, as models increase in size—consider the hefty 175 billion parameters of GPT-3—this approach can easily lead to overfitting and is costly in terms of both computation and storage when needed for diverse applications. Parameter-Efficient Transfer Learning emerges as a strategic solution, aiming to adapt these powerful models in a way that requires altering as few parameters as possible. This methodology is inspired by breakthroughs within the NLP field and is gaining traction in visual and multimodal research.
Keywords Convention
The project embraces a systematic labeling approach to classify papers, drawing from the methodology developed by the PromptPapers project. These labels help in quickly identifying the focus and contributions of each work, using visual badges for easy reference. For instance, tags like 
Featured Papers
Prompt-based Papers
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Learning to Prompt for Vision-Language Models: This paper explores the application of text prompts to improve image classification by leveraging vision-language models. Authors Kaiyang Zhou et al. demonstrate how strategic prompting can enhance model adaptability.
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Prompting Visual-Language Models for Efficient Video Understanding: Authored by Chen Ju et al., this study focuses on the use of text prompts to streamline processes such as action recognition, localization, and text-video retrieval.
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Domain Adaptation via Prompt Learning: Chunjiang Ge and colleagues tackle the challenge of domain adaptation, proposing a prompt learning framework to shuffle knowledge across varied domains.
Adapter-based Approaches
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P2P: Tuning Pre-trained Image Models for Point Cloud Analysis: This work by Ziyi Wang et al. suggests an innovative "point-to-pixel" prompting technique to transition from 2D images to 3D point cloud data, enhancing the versatility of pre-trained models.
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LLaMA-Adapter: Renrui Zhang and the team present an efficient approach to fine-tune the LLaMA model, transforming it into a chatbot with a mere 1.2 million trainable parameters, accomplished in an hour, demonstrating significant resource efficiency.
Unified and Other Methods
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MaPLe: Multi-modal Prompt Learning: This study introduces a multi-modal prompt learning framework that addresses image classification with attention to pixel-level detail.
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Probabilistic Prompt Learning for Dense Prediction: Hyeongjun Kwon and colleagues delve into dense prediction tasks, offering a probabilistic perspective to guide prompt tuning effectively.
Contributions and Community
The project actively encourages contributions from the wider research community. Contributors are welcome to aid in expanding this curated list of papers, thereby fostering a rich resource for ongoing and future research within the field.
Acknowledgments
The project extends gratitude to contributors and collaborators who have enriched this repository with their insightful research and feedback, ensuring that it remains a pivotal resource for understanding and advancing parameter-efficient transfer learning.
By consolidating and categorizing significant research efforts, the Awesome-Parameter-Efficient-Transfer-Learning project offers invaluable resources and insights for researchers aiming to innovate and employ transfer learning with an emphasis on parameter efficiency.