The learnware paradigm has been proposed as a new manner for reusing models from a market of various well-trained models, which can relieve users’ burden of training a new model from scratch. A learnware consists of a well-trained model and a specification which explains the purpose or specialty of the model without revealing data. By specification matching, the market can identify the most useful learnwares for users’ tasks. Prior art attempted to generate the specification by a reduced kernel mean embedding approach. However, such kind of specification is defined by some pre-designed kernel function, which lacks flexibility. In this paper, we advance a methodology for direct specification learning from data, introducing a novel neural network named SpecNet for this purpose. Our approach accepts unordered datasets as input and subsequently produces specification vectors in a latent space. Notably, the flexibility and efficiency of our learned specifications are underscored by their derivation from diverse tasks, rendering them particularly adept for learnware identification. Empirical studies provide validation for the efficacy of our proposed approach.
