Software & Data Downloads — ACOT

In this software release, we provide a PyTorch implementation of the adversarially-contrastive optimal transport (ACOT) algorithm. Through ACOT, we study the problem of learning compact representations for sequential data that captures its implicit spatio-temporal cues. To separate such informative cues from the data, we propose a novel contrastive learning objective via optimal transport. Specifically, our formulation seeks a low-dimensional subspace representation of the data that jointly (i) maximizes the distance of the data (embedded in this subspace) from an adversarial data distribution under a Wasserstein distance, (ii) captures the temporal order, and (iii) minimizes the data distortion. To generate the adversarial distribution, we propose to use a Generative Adversarial Network (GAN) with novel regularizers. Our full objective can be cast as a subspace learning problem on the Grassmann manifold, and can be solved efficiently via Riemannian optimization. The associated software implements all components of our algorithm.

Access software at https://github.com/merlresearch/AdvConOT.