Asymmetric Similarity

Asymmetric similarity focuses on modeling relationships where the similarity between two entities is not reciprocal, unlike traditional symmetric approaches. Current research emphasizes developing models, often employing transformer-based architectures or convolutional neural networks, that effectively capture this asymmetry in diverse applications like image retrieval and network embedding. This nuanced approach improves performance in tasks where directional relationships are crucial, such as image copy detection and node clustering in heterophilous graphs, leading to more accurate and efficient solutions in various fields. The development of memory-efficient methods is also a key focus, particularly for large-scale applications.