#Classification

This compilation provides comprehensive materials on semi-supervised learning, aimed at researchers and practitioners interested in using unlabeled data to enhance classification models. It includes diverse methods like EM mixture models and graph-based strategies integrated into deep learning frameworks for efficient and accurate model training. Covering applications in computer vision, NLP, and reinforcement learning, this resource supports theoretical and practical advancements in AI. Community contributions are encouraged for ongoing updates.

The toolkit incorporates HuggingFace transformers to integrate multimodal data, enhancing tasks like classification and regression with a combination of categorical, numerical, and text features. It supports transformers such as BERT and RoBERTa and offers detailed datasets and examples for configuration. Suitable for AI applications requiring comprehensive data input, it provides flexible and robust methodologies for predictive modeling. Refer to the blog post and Colab notebook for detailed use cases.

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.