Pre Trained Feature Extractor

Pre-trained feature extractors are foundational models used to extract meaningful features from data, primarily images and videos, before feeding them into downstream tasks like classification or anomaly detection. Current research emphasizes optimizing these extractors for specific applications, exploring various architectures (including CNNs, Transformers, and predictive coding networks) and training methods (such as self-supervised, contrastive, and masked autoencoding learning) to improve performance and robustness across diverse domains and data limitations. This work is crucial for advancing numerous fields, from medical image analysis and industrial anomaly detection to video understanding and natural language processing, by providing efficient and effective feature representations that reduce the need for extensive labeled data in downstream tasks.