Data Efficient Deep Learning

Data-efficient deep learning focuses on training accurate deep learning models with minimal labeled data, addressing the limitations of data scarcity in many domains. Current research emphasizes techniques like incorporating inductive biases through novel architectures, leveraging additional data sources beyond class labels (e.g., concepts), and employing advanced data augmentation and ensemble methods. These advancements are crucial for applications where large datasets are unavailable or expensive to acquire, such as medical image analysis and Earth observation, enabling broader deployment of deep learning in resource-constrained settings.