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arXiv · 1907.11943

Learnable Parameter Similarity

Guangcong Wang·Jianhuang Lai·Wenqi Liang·Guangrun Wang
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Abstract

Most of the existing approaches focus on specific visual tasks while ignoring the relations between them. Estimating task relation sheds light on the learning of high-order semantic concepts, e.g., transfer learning. How to reveal the underlying relations between different visual tasks remains largely unexplored. In this paper, we propose a novel \textbf{L}earnable \textbf{P}arameter \textbf{S}imilarity (\textbf{LPS}) method that learns an effective metric to measure the similarity of second-order semantics hidden in trained models. LPS is achieved by using a second-order neural network to align high-dimensional model parameters and learning second-order similarity in an end-to-end way. In addition, we create a model set called ModelSet500 as a parameter similarity learning benchmark that contains 500 trained models. Extensive experiments on ModelSet500 validate the effectiveness of the proposed method. Code will be released at \url{https://github.com/Wanggcong/learnable-parameter-similarity}.

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Guangcong Wang, Jianhuang Lai, Wenqi Liang, Guangrun Wang. 2019-07-27. Learnable Parameter Similarity. https://arxiv.org/abs/1907.11943

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