#Parameter-Efficient Transfer Learning

This repository provides vital resources for tools and papers on adapter methods, termed Parameter-Efficient Transfer Learning (PETL) or Parameter-Efficient Fine-Tuning (PEFT), aimed at adapting large pre-trained neural networks. Adapters are advantageous in terms of parameter efficiency, modularity, and scalability, enabling models to be adapted to new tasks with minimal parameter adjustments. It includes frameworks, tools, and surveys for NLP, computer vision, and audio processing, highlighting cutting-edge techniques and their practical applications. Discover dynamic frameworks such as AdapterHub and innovative approaches like Low-Rank Adaptation (LoRA) for effective optimization of large language models.

Examine the collection of papers on parameter-efficient transfer learning aimed at computer vision and multimodal fields. The collection focuses on methods for efficiently adapting large-scale pre-trained models, minimizing overfitting risks and storage requirements associated with comprehensive fine-tuning. Utilizing insights from NLP, the papers enhance applications in image classification, prompt learning, and multimodal tasks. This project provides a thorough overview of advancements and methodologies in optimizing transfer learning across various computational landscapes.

Investigate a benchmark for parameter-efficient transfer learning in computer vision, assessing 25 leading algorithms on 30 varied datasets. The platform provides a modular codebase for comprehensive analysis in image recognition, video action recognition, and dense prediction. Pre-trained models like ViT and Swin are used to attain high performance with fewer parameters. The benchmark facilitates easy evaluation and continuous updates for new PETL methods and applications.