Representation Gap

The "representation gap" refers to discrepancies in how data is represented across different models, datasets, or even between humans and artificial intelligence. Current research focuses on mitigating these gaps, particularly in areas like anomaly detection (using techniques like feature attenuation), fairness and bias in datasets (analyzing filtering biases in data creation), and improving the generalizability of deep learning models (comparing human and AI learning processes). Addressing representation gaps is crucial for building more robust, equitable, and efficient AI systems, impacting various fields from computer vision and natural language processing to healthcare and social sciences.