4 papers · 1 filter
WeightCLIP: Aligning Datasets and Models for Weight Space Learning
Aron Asefaw, Konstantinos Tzevelekakis, Damian Falk +2
Weight space learning aims to learn representations of neural network (NN) weights, enabling different downstream tasks. Existing approaches show promising performance, but lacking…
Learning Model Representations Using Publicly Available Model Hubs
Damian Falk, Konstantin Schürholt, Konstantinos Tzevelekakis +2
The weights of neural networks have emerged as a novel data modality, giving rise to the field of weight space learning. A central challenge in this area is that learning meaningfu…
A Model Zoo of Vision Transformers
Damian Falk, Léo Meynent, Florence Pfammatter +2
The availability of large, structured populations of neural networks - called 'model zoos' - has led to the development of a multitude of downstream tasks ranging from model analys…
The Impact of Model Zoo Size and Composition on Weight Space Learning
Damian Falk, Konstantin Schürholt, Damian Borth
Re-using trained neural network models is a common strategy to reduce training cost and transfer knowledge. Weight space learning - using the weights of trained models as data moda…