Familiarity Based

Familiarity-based approaches in machine learning aim to leverage a model's understanding of known data to identify novel or unexpected inputs. Current research focuses on developing robust familiarity metrics, often incorporating complexity measures alongside traditional approaches like perplexity, and applying these to diverse tasks such as open-set recognition and user experience analysis in virtual reality. These methods show promise in improving model performance and generalization, particularly in scenarios with limited or biased training data, and are being explored across various model architectures, including transformers and deep classifiers. The ultimate goal is to create more reliable and adaptable systems capable of handling unforeseen situations in real-world applications.