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Model Zoos: A Dataset of Diverse Populations of Neural Network Models
Konstantin Schürholt · Diyar Taskiran · Boris Knyazev · Xavier Giro-i-Nieto · Damian Borth

Wed Nov 30 09:00 AM -- 11:00 AM (PST) @ Hall J #1017

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 significantly increases the friction for further research about populations of NNs. With this work, we publish a novel dataset of model zoos containing systematically generated and diverse populations of NN models for further research. In total the proposed model zoo dataset is based on eight image datasets, consists of 27 model zoos trained with varying hyperparameter combinations and includes 50’360 unique NN models as well as their sparsified twins, resulting in over 3’844’360 collected model states. Additionally, to the model zoo data we provide an in-depth analysis of the zoos and provide benchmarks for multiple downstream tasks. The dataset can be found at www.modelzoos.cc.

Author Information

Konstantin Schürholt (University of St. Gallen)
Diyar Taskiran (Universität St. Gallen)
Boris Knyazev (University of Guelph)
Xavier Giro-i-Nieto (UPC Barcelona)

Xavier Giro-i-Nieto is an associate professor at the Universitat Politecnica de Catalunya (UPC). He graduated in Electrical Engineering studies at ETSETB (UPC) in 2000, after completing his master thesis on image compression at the Vrije Universiteit in Brussels (VUB) under the direction of Professor Peter Schelkens. In 2001 he worked in the digital television group of Sony Brussels, before returning to Barcelona and joining the Image Processing Group at the UPC. Since 2003, he has created and taught graduate and undergraduate courses for Electrical Engineering degress at the ESEIAAT and ETSETB schools from UPC. In 2013 he participated in the design of the Master in Computer Vision of Barcelona by UPC, UAB, UPF and UOC universities, where he lectures on deep learning, image retrieval and video processingl. He has taught several international courses in the framework of the European Erasmus program. He obtained his Phd on image retrieval in 2012, under the supervision by Professor Ferran Marqués from UPC and Professor Shih-Fu Chang from Columbia University. He was a visiting scholar during Summers 2008 to 2014 at the Digital Video and MultiMedia laboratory at Columbia University, in New York. His relation with industry includes collaborations with Mediapro, Catalan Broadcast Corporation (TV3), Pixable, Catchoom and Narrative.

Damian Borth (University of St.Gallen (HSG))

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