Revolutionizing Similarity Measurement: Tversky Neural Networks
2025-08-17
This paper introduces a novel neural network architecture based on Tversky similarity, challenging the prevalent use of dot product or cosine similarity in deep learning. It elegantly transforms the traditionally discrete set operations of the Tversky model into differentiable functions, enabling training within the deep learning framework. Experiments demonstrate significant performance improvements in image recognition and language modeling, alongside enhanced interpretability, allowing for intuitive explanations of model decisions. The core innovation lies in a differentiable Tversky similarity function that considers both common and distinctive features, aligning better with human perception of similarity.
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