Software & Data Downloads — ACOT

Adversarially-Contrastive Optimal Transport for studying the problem of learning compact representations for sequential data that captures its implicit spatio-temporal cues.

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.

    •  Cherian, A., Aeron, S., "Representation Learning via Adversarially-Contrastive Optimal Transport", International Conference on Machine Learning (ICML), Daumé, H. and Singh, A., Eds., July 2020, pp. 10675-10685.
      BibTeX TR2020-093 PDF Software
      • @inproceedings{Cherian2020jul,
      • author = {Cherian, Anoop and Aeron, Shuchin},
      • title = {{Representation Learning via Adversarially-Contrastive Optimal Transport}},
      • booktitle = {International Conference on Machine Learning (ICML)},
      • year = 2020,
      • editor = {Daumé, H. and Singh, A.},
      • pages = {10675--10685},
      • month = jul,
      • url = {https://www.merl.com/publications/TR2020-093}
      • }

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