Lovasz Theta Contrastive Learning

Georgios Smyrnis, Matt Jordan, Ananya Uppal, Giannis Daras, Alex Dimakis

Keywords: Lovasz theta, Contrastive learning, Similarity graph, Graph Theory

Abstract: We establish a connection between the Lovasz theta function of a graph and the widely used InfoNCE loss. We show that under certain conditions, the minima of the InfoNCE loss are related to minimizing the Lovasz theta function on the empty similarity graph between the samples. Building on this connection, we generalize contrastive learning on weighted similarity graphs between samples. Our Lovasz theta contrastive loss uses a weighted graph that can be learned to take into account similarities between our data. We evaluate our method on image classification tasks, demonstrating an improvement of $1 \%$ in the supervised case and up to $4 \%$ in the unsupervised case.

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Please Choose The Closest Area That Your Submission Falls Into: Unsupervised and Self-supervised learning