Do Deep Neural Network Solutions Form a Star Domain?
Ankit Sonthalia Alexander Rubinstein Ehsan Abbasnejad Seong Joon Oh
It has recently been conjectured that neural network solution sets reachable via stochastic gradient descent (SGD) are convex, considering permutation invariances. This means that a linear path can connect two independent solutions with low loss, given the weights of one of the models are appropriately permuted. However, current methods to test this theory often require very wide networks to succeed. In this work, we conjecture that more generally, the SGD solution set is a star domain that contains a star model that is linearly connected to all the other solutions via paths with low loss values, modulo permutations. We propose the Starlight algorithm that finds a star model of a given learning task. We validate our claim by showing that this star model is linearly connected with other independently found solutions. As an additional benefit of our study, we demonstrate better uncertainty estimates on Bayesian Model Averaging over the obtained star domain. Further, we demonstrate star models as potential substitutes for model ensembles.
mode connectivity, loss landscapes, neural networks, parameter space, star domain
We conjecture that SGD solution sets for DNNs are star-domains modulo permutation invariances.