Similarity of Neural Networks with Gradients

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Authors Andreas Damianou, Shuai Tang, Wesley J. Maddox, Charlie Dickens, Tom Diethe
Journal/Conference Name arXiv preprint
Paper Category
Paper Abstract A suitable similarity index for comparing learnt neural networks plays an important role in understanding the behaviour of the highly-nonlinear functions, and can provide insights on further theoretical analysis and empirical studies. We define two key steps when comparing models firstly, the representation abstracted from the learnt model, where we propose to leverage both feature vectors and gradient ones (which are largely ignored in prior work) into designing the representation of a neural network. Secondly, we define the employed similarity index which gives desired invariance properties, and we facilitate the chosen ones with sketching techniques for comparing various datasets efficiently. Empirically, we show that the proposed approach provides a state-of-the-art method for computing similarity of neural networks that are trained independently on different datasets and the tasks defined by the datasets.
Date of publication 2020
Code Programming Language Python

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