Model Zoos: A Dataset of Diverse Populations of Neural Network Models
Konstantin Sch\"urholt, Diyar Taskiran, Boris Knyazev, Xavier, Gir\'o-i-Nieto, Damian Borth

TL;DR
This paper introduces a comprehensive dataset of neural network model zoos, capturing diverse populations of models trained on various datasets, enabling new research avenues in model analysis, dynamics, and generative modeling.
Contribution
It provides the first large-scale, systematically generated dataset of neural network populations, including detailed analysis and benchmarks for downstream tasks.
Findings
The dataset contains over 3.8 million model states.
Analysis reveals structural properties of model populations.
Benchmarks demonstrate utility for various tasks.
Abstract
In the last years, neural networks (NN) have evolved from laboratory environments to the state-of-the-art for many real-world problems. It was shown that NN models (i.e., their weights and biases) evolve on unique trajectories in weight space during training. Following, a population of such neural network models (referred to as model zoo) would form structures in weight space. We think that the geometry, curvature and smoothness of these structures contain information about the state of training and can reveal latent properties of individual models. With such model zoos, one could investigate novel approaches for (i) model analysis, (ii) discover unknown learning dynamics, (iii) learn rich representations of such populations, or (iv) exploit the model zoos for generative modelling of NN weights and biases. Unfortunately, the lack of standardized model zoos and available benchmarks…
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Taxonomy
TopicsNeural Networks and Applications · Advanced Neural Network Applications · Generative Adversarial Networks and Image Synthesis
