#Domain Adaptation

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.

Find a curated collection of resources on test-time adaptation methods, such as source-free domain, batch, instance, online, and prior adaptations. The repository includes datasets and critical insights for researchers dealing with distribution shifts. Improve understanding of methods applied in test phases and keep updated with recent advancements in the field. Engage with state-of-the-art solutions for adapting machine learning models to new data without retraining.

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.

This resource delves into continual test-time domain adaptation by comparing methods such as AdaBN, BN Adapt, and TENT. It covers tasks from CIFAR10/100 to ImageNet and Cityscapes to ACDC, aimed at optimizing classification and segmentation models. Learn about experimental setups including environment preparation via Conda, and experiment scripts tested on systems like RTX2080TI and RTX3090. Additional links provide access to supplementary materials for a thorough understanding of implementation and methodology.