Unseen Bird Specie

Research on unseen species identification focuses on developing computational methods to recognize and analyze new instances of biological entities (e.g., proteins, birds) not present in training data. Current efforts leverage deep learning architectures, including generative models (like diffusion transformers and flow-based models), and employ techniques such as zero-shot learning and few-shot learning to address data scarcity. These advancements are improving the accuracy of protein structure prediction, protein-protein interaction identification, and species classification from limited data, with implications for drug discovery, biodiversity monitoring, and other scientific domains.