Conditional Similarity

Conditional similarity focuses on learning flexible representations that adapt to different notions of similarity, moving beyond single, fixed embedding spaces. Current research emphasizes developing models that can dynamically adjust to various conditions, such as focusing on specific features (e.g., instrument type in music, color in images) or semantic relationships, often employing triplet loss functions and conditional similarity networks. This work is significant for improving the accuracy and adaptability of similarity-based applications across diverse domains, including music recommendation, image retrieval, and fashion compatibility prediction.