Feature Extractor

Feature extractors are crucial components of machine learning models, tasked with transforming raw data (images, text, audio, etc.) into meaningful representations for downstream tasks like classification or prediction. Current research emphasizes improving feature extractors' performance across diverse data types and model architectures, including convolutional neural networks (CNNs), vision transformers (ViTs), and foundation models, often focusing on techniques like self-supervised learning, prompt engineering, and multi-modal fusion. These advancements are driving progress in various fields, from medical image analysis and biomarker prediction to activity recognition and recommendation systems, by enabling more accurate and efficient models.