#Transfer Learning

Delve into an extensive collection of transfer learning resources, featuring academic papers, tutorials, and code implementations. Stay informed about cutting-edge developments and foundational theories in domain adaptation, deep transfer learning, and multi-task learning. Access various educational materials, including video tutorials, profiles of eminent scholars, and prominent research papers, offering essential insights for both newcomers and experienced researchers.

This literature review examines the development of recommender systems with a focus on foundational models that do not rely on explicit ID features. It discusses the potential for these systems to evolve independently, akin to foundational models in natural language processing and computer vision, and the ongoing debate regarding the necessity of ID embeddings. The review further explores how Large Language Models (LLMs) may transform recommender systems by shifting focus from matching to generative paradigms. Additionally, it highlights advancements in multimodal and transferable recommender systems, offering insights from empirical research into universal user representation. This review serves as a comprehensive guide to understanding current trends and future directions in the field of recommender systems.

Discover the latest advancements in large-scale multi-modality models, efficient parameter finetuning, and vision-language pretraining. Access frequently updated resources in image-text matching and relevant technologies, catered for academic and professional audiences interested in emerging AI approaches.

This project provides a well-structured codebase enabling the training of conversational agents via transfer learning from OpenAI's GPT and GPT-2 models. It replicates HuggingFace's successful outcomes from the NeurIPS 2018 ConvAI2 competition, simplifying over 3,000 lines of competition code into a concise 250-line script, optimized for distributed and FP16 training. The model can be trained on cloud instances within an hour, with a pre-trained version readily available for immediate deployment. The project includes setup instructions, Docker support, and detailed guidance for training, interaction, and evaluation, thus offering a comprehensive solution for creating cutting-edge conversational AI.

VPGTrans provides a method to reduce computational costs in vision-language model development by transferring visual prompt generators between large language models, decreasing GPU usage and training data requirements while maintaining performance. Illustrative models include VL-LLaMA and VL-Vicuna, demonstrating diverse applications. Released with comprehensive source codes, VPGTrans enhances the scalability and flexibility of building and customizing vision-language models efficiently.

Offsite-Tuning presents an innovative transfer learning framework designed to enhance privacy and computational efficiency. It enables the adaptation of large-scale foundation models to specific tasks without requiring full model access, effectively addressing traditional cost and privacy concerns. A lightweight adapter and a compressed emulator are provided for local fine-tuning, maintaining accuracy while significantly improving speed and reducing memory usage. This approach is validated on various large language and vision models, providing a practical solution for environments prioritizing privacy and resource constraints.

Adapters enrich HuggingFace's Transformers by integrating over 10 adapter methods into 20+ models, supporting efficient fine-tuning and transfer learning. Key features include full-precision and quantized training, adapter task arithmetics, and multi-adapter compositions, facilitating advanced research in NLP. Compatible with Python 3.8+ and PyTorch 1.10+, it's an essential tool for optimizing models with ease of implementation.

Explore the TensorFlow 101 project to learn various deep learning applications with comprehensive tutorials and practical examples. Diverse models like VGG-Face and FaceNet are utilized in tasks such as facial recognition and emotion analysis.

Access a comprehensive collection on parameter-efficient transfer learning, emphasizing fine-tuning techniques for pre-trained vision models. Discover key papers and methodologies, including adapter and prompt tuning. Keep updated with developments like the Visual PEFT Library/Benchmark release. This repository is perfect for researchers aiming to optimize transfer learning processes.

This directory provides a well-organized collection of important natural language processing (NLP) research papers, including significant topics like Transformer frameworks, BERT variations, transfer learning, text summarization, sentiment analysis, question answering, and machine translation. It features notable works such as 'Attention Is All You Need' and detailed investigations into BERT's functions. Covering downstream tasks like QA and dialogue systems, interpretable machine learning, and specialized applications, this collection is a valuable resource for researchers and developers exploring advancements and techniques influencing current NLP practices, with a focus on practical implications in machine learning.

The Transfer-Learning-Library (TLlib) is a high-performance PyTorch-based resource designed for Transfer Learning. It features a comprehensive API for methods including domain alignment, translation, and self-training. Ideal for implementing or applying algorithms across classification, regression, and other tasks, TLlib aligns with torchvision's structure, facilitating easy development of transfer learning solutions. Extensive documentation supports various learning setups such as domain and task adaptation, enhancing deep learning research objectively.

Models Genesis offers self-supervised pre-trained models for 3D medical imaging, optimizing transfer learning with limited data. Developed collaboratively by top institutions, it has garnered significant awards and supports key frameworks like Keras and PyTorch, excelling in medical image segmentation tasks.