Disentangling Linear Mode-Connectivity
Gul Sena Altintas Gregor Bachmann Lorenzo Noci Thomas Hofmann
Linear mode-connectivity (LMC) (or lack thereof) is one of the intriguing
characteristics of neural network loss landscapes. While empirically well
established, it unfortunately still lacks a proper theoretical understanding.
Even worse, although empirical data points are abound, a systematic study of
when networks exhibit LMC is largely missing in the literature. In this work we
aim to close this gap. We explore how LMC is affected by three factors: (1)
architecture (sparsity, weight-sharing), (2) training strategy (optimization
setup) as well as (3) the underlying dataset. We place particular emphasis on
minimal but non-trivial settings, removing as much unnecessary complexity as
possible. We believe that our insights can guide future theoretical works on
uncovering the inner workings of LMC.