Model-invariant Weight Distribution Descriptors for Visual Exploration of Neural Networks en Masse
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
We present a neural network representation which can be used for visually analyzing the similarities and differences in a large corpus of trained neural networks. The focus is on architecture-invariant comparisons based on network weights, estimating similarities of the statistical footprints encoded by the training setups and stochastic optimization procedures. To make this possible, we propose a novel visual descriptor of neural network weights. The visual descriptor considers local weight statistics in a model-agnostic manner by encoding the distribution of weights over different model depths. We show how such a representation can extract descriptive information, is robust to different parameterizations of a model, and is applicable to different architecture specifications. The descriptor is used to create a model atlas by projecting a model library to a 2D representation, where clusters can be found based on similar weight properties. A cluster analysis strategy makes it possible to understand the weight properties of clusters and how these connect to the different datasets and hyper-parameters used to train the models.
Description
@inproceedings{10.2312:evs.20241068,
booktitle = {EuroVis 2024 - Short Papers},
editor = {Tominski, Christian and Waldner, Manuela and Wang, Bei},
title = {{Model-invariant Weight Distribution Descriptors for Visual Exploration of Neural Networks en Masse}},
author = {Eilertsen, Gabriel and Jönsson, Daniel and Unger, Jonas and Ynnerman, Anders},
year = {2024},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-251-6},
DOI = {10.2312/evs.20241068}
}