no code implementations • CVPR 2015 • Soheil Kolouri, Gustavo K. Rohde
Extracting high-resolution information from highly degraded facial images is an important problem with several applications in science and technology.
no code implementations • CVPR 2016 • Soheil Kolouri, Yang Zou, Gustavo K. Rohde
Optimal transport distances, otherwise known as Wasserstein distances, have recently drawn ample attention in computer vision and machine learning as a powerful discrepancy measure for probability distributions.
no code implementations • 10 Nov 2015 • Soheil Kolouri, Se Rim Park, Gustavo K. Rohde
Invertible image representation methods (transforms) are routinely employed as low-level image processing operations based on which feature extraction and recognition algorithms are developed.
no code implementations • 15 Sep 2016 • Soheil Kolouri, Serim Park, Matthew Thorpe, Dejan Slepčev, Gustavo K. Rohde
Transport-based techniques for signal and data analysis have received increased attention recently.
no code implementations • 27 Sep 2016 • Matthew Thorpe, Serim Park, Soheil Kolouri, Gustavo K. Rohde, Dejan Slepčev
Transport based distances, such as the Wasserstein distance and earth mover's distance, have been shown to be an effective tool in signal and image analysis.
no code implementations • 14 May 2017 • Shinjini Kundu, Soheil Kolouri, Kirk I Erickson, Arthur F Kramer, Edward McAuley, Gustavo K. Rohde
Disease in the brain is often associated with subtle, spatially diffuse, or complex tissue changes that may lie beneath the level of gross visual inspection, even on magnetic resonance imaging (MRI).
2 code implementations • CVPR 2018 • Soheil Kolouri, Gustavo K. Rohde, Heiko Hoffmann
In contrast to the KL-divergence, the energy landscape for the sliced-Wasserstein distance is more well-behaved and therefore more suitable for a stochastic gradient descent scheme to obtain the optimal GMM parameters.
no code implementations • 20 Feb 2018 • Liam Cattell, Gustavo K. Rohde
In this work we describe an approach for simultaneous modeling and inference of such data, using the mathematics of optimal transport.
5 code implementations • 5 Apr 2018 • Soheil Kolouri, Phillip E. Pope, Charles E. Martin, Gustavo K. Rohde
In short, we regularize the autoencoder loss with the sliced-Wasserstein distance between the distribution of the encoded training samples and a predefined samplable distribution.
1 code implementation • NeurIPS 2019 • Soheil Kolouri, Kimia Nadjahi, Umut Simsekli, Roland Badeau, Gustavo K. Rohde
The SW distance, specifically, was shown to have similar properties to the Wasserstein distance, while being much simpler to compute, and is therefore used in various applications including generative modeling and general supervised/unsupervised learning.
1 code implementation • ICLR 2019 • Soheil Kolouri, Phillip E. Pope, Charles E. Martin, Gustavo K. Rohde
In this paper we use the geometric properties of the optimal transport (OT) problem and the Wasserstein distances to define a prior distribution for the latent space of an auto-encoder.
1 code implementation • 27 May 2019 • Xuwang Yin, Soheil Kolouri, Gustavo K. Rohde
The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains.
1 code implementation • 7 Jun 2019 • Mohammad Shifat-E-Rabbi, Xuwang Yin, Cailey Elizabeth Fitzgerald, Gustavo K. Rohde
Cell image classification methods are currently being used in numerous applications in cell biology and medicine.
1 code implementation • 4 Jul 2019 • Soheil Kolouri, Xuwang Yin, Gustavo K. Rohde
Connections between integration along hypersufaces, Radon transforms, and neural networks are exploited to highlight an integral geometric mathematical interpretation of neural networks.
3 code implementations • 7 Apr 2020 • Mohammad Shifat-E-Rabbi, Xuwang Yin, Abu Hasnat Mohammad Rubaiyat, Shiying Li, Soheil Kolouri, Akram Aldroubi, Jonathan M. Nichols, Gustavo K. Rohde
We present a new supervised image classification method applicable to a broad class of image deformation models.
no code implementations • ICLR 2020 • Xuwang Yin, Soheil Kolouri, Gustavo K. Rohde
The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains.
1 code implementation • ICLR 2021 • Soheil Kolouri, Navid Naderializadeh, Gustavo K. Rohde, Heiko Hoffmann
We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast framework for embedding entire graphs in a vector space, in which various machine learning models are applicable for graph-level prediction tasks.
Ranked #3 on Graph Classification on RE-M5K
no code implementations • 8 Aug 2020 • Akram Aldroubi, Shiying Li, Gustavo K. Rohde
A relatively new set of transport-based transforms (CDT, R-CDT, LOT) have shown their strength and great potential in various image and data processing tasks such as parametric signal estimation, classification, cancer detection among many others.
1 code implementation • 11 Dec 2020 • Xuwang Yin, Shiying Li, Gustavo K. Rohde
We study a new approach to learning energy-based models (EBMs) based on adversarial training (AT).
1 code implementation • 3 Jun 2021 • Akram Aldroubi, Rocio Diaz Martin, Ivan Medri, Gustavo K. Rohde, Sumati Thareja
This paper presents a new mathematical signal transform that is especially suitable for decoding information related to non-rigid signal displacements.
1 code implementation • 11 Oct 2021 • Abu Hasnat Mohammad Rubaiyat, Mohammad Shifat-E-Rabbi, Yan Zhuang, Shiying Li, Gustavo K. Rohde
This paper presents a new method to classify 1D signals using the signed cumulative distribution transform (SCDT).
2 code implementations • 9 Jan 2022 • Mohammad Shifat E Rabbi, Yan Zhuang, Shiying Li, Abu Hasnat Mohammad Rubaiyat, Xuwang Yin, Gustavo K. Rohde
However, they are known to underperform when training data are limited and thus require data augmentation strategies that render the method computationally expensive and not always effective.
1 code implementation • 22 Feb 2022 • Yan Zhuang, Shiying Li, Mohammad Shifat-E-Rabbi, Xuwang Yin, Abu Hasnat Mohammad Rubaiyat, Gustavo K. Rohde
Face recognition is then performed using a nearest subspace in R-CDT domain of local gradient distributions.
1 code implementation • 30 Apr 2022 • Abu Hasnat Mohammad Rubaiyat, Shiying Li, Xuwang Yin, Mohammad Shifat E Rabbi, Yan Zhuang, Gustavo K. Rohde
This paper presents a new end-to-end signal classification method using the signed cumulative distribution transform (SCDT).
no code implementations • 4 Jun 2022 • Shiying Li, Abu Hasnat Mohammad Rubaiyat, Gustavo K. Rohde
Transport-based metrics and related embeddings (transforms) have recently been used to model signal classes where nonlinear structures or variations are present.
1 code implementation • 28 Jul 2023 • Le Gong, Shiying Li, Naqib Sad Pathan, Mohammad Shifat-E-Rabbi, Gustavo K. Rohde, Abu Hasnat Mohammad Rubaiyat, Sumati Thareja
Here we describe a new image representation technique based on the mathematics of transport and optimal transport.
1 code implementation • 23 Aug 2023 • Abu Hasnat Mohammad Rubaiyat, Duy H. Thai, Jonathan M. Nichols, Meredith N. Hutchinson, Samuel P. Wallen, Christina J. Naify, Nathan Geib, Michael R. Haberman, Gustavo K. Rohde
This paper presents a novel data-driven approach to identify partial differential equation (PDE) parameters of a dynamical system.
no code implementations • 9 Oct 2023 • Rocio Diaz Martin, Ivan Medri, Yikun Bai, Xinran Liu, Kangbai Yan, Gustavo K. Rohde, Soheil Kolouri
The optimal transport problem for measures supported on non-Euclidean spaces has recently gained ample interest in diverse applications involving representation learning.