Single Sample

Single-sample learning focuses on developing methods that effectively learn from minimal data, addressing limitations in data availability and resource constraints across various machine learning applications. Current research emphasizes techniques like generative models for data expansion, adaptive inference methods that adjust to individual test samples, and algorithms designed for efficient learning in decentralized or federated settings. This field is crucial for advancing machine learning in resource-limited environments and improving the efficiency and robustness of models across diverse domains, including medical imaging, drug discovery, and reinforcement learning.