Litu Rout

I am a second year PhD student at The University of Texas Austin, advised by Prof. Constantine Caramanis and Prof. Sanjay Shakkottai. My current research focuses on the theoretical foundations of diffusion models, generative modeling, posterior sampling, and stochastic optimization. I am currently working as a student researcher at Google Research.

Prior to UT Austin, I worked as a Scientist/Engineer-SD at the Indian Space Research Organisation, where I developed operational deep learning algorithms and analyzed their convergence properties.

I received my BTech from the Indian Institute of Space Science and Technology (IIST). I was fortunate to be advised by Prof. Rama Krishna Gorthi and Prof. Deepak Mishra during my undergraduate research.

Contact  /  Google Scholar  /  DBLP  /  LinkedIn  /  GitHub

• Feb 2024: One paper Beyond First-order Tweedie: Solving Inverse Problems using Latent Diffusion accepted at CPVR 2024!
• Nov 2023: New preprint: Beyond First-order Tweedie: Solving Inverse Problems using Latent Diffusion!
• Sep 2023: One paper Solving Linear Inverse Problems Provably using PSLD accepted at NeurIPS 2023!
• Feb 2023: One paper Beyond Uniform Smoothness: A Stopped Analysis of Adaptive SGD accepted at COLT 2023.
• Feb 2023: New preprint: A Theoretical Justification for Image Inpainting using Diffusion Models.
• Jan 2023: One paper Hierarchical Sliced Wasserstein Distance accepted at ICLR 2023.
• Sep 2022: Received Cockrell School of Engineering Fellowship from UT Austin.
• Apr 2022: Presented Generative Modeling with Optimal Transport Maps at ICLR 2022.
• Apr 2021: Published a paper on Phobos image enhancement at Planetary and Space Science Journal.
• Feb 2021: Presented a paper on Turing Instability in Adversarial Learning at AAAI 2021 .
• Dec 2020: Presented the Pseudo-Reaction-Diffusion paper at the NeurIPS workshop on ML4PS.
• Sep 2020: Patent granted: A Method for Sequential Information Condensation using Fourier Basis.
• Jun 2020: Presented two papers at the CVPR workshop on Large Scale Computer Vision for Remote Sensing Imagery.
• Jan 2020: Published ALERT in the IEEE Transactions on Geoscience and Remote Sensing, impact factor: 5.85.
• Dec 2018: Received the Innovative Student Project Award for excellence in engineering and technology.

Neurips (2022, 2023), ICLR (2022,2023,2024), ICML (2022,2023,2024), CVPR (2024), TMLR (2024), AISTATS (2023), Pattern Recognition (2022), NeurIPS ML4PS (2021).

We present an efficient second-order approximation using Tweedie's formula to mitigate the bias incurred in the widely used first-order samplers. With this method, we devise a surrogate loss function to refine the reverse process at every diffusion step to address inverse problems and perform high-fidelity text-guided image editing.

Solving inverse problems (e.g. inpainting/deblurring) for general domain images is hard. Magic Eraser and other commercial tools use separately trained models for each task. We introduce PSLD, a method that uses Stable Diffusion to solve all linear problems without any extra training.

We provide a theoretical justification for sample recovery using diffusion based image inpainting in a linear model setting. Unlike most inpainting algorithms, we prove that diffusion based inpainting generalizes well to unseen masks without retraining. Motivated by our analysis, we propose a modified RePaint algorithm we call RePaint+ that provably recovers the underlying true sample and enjoys a linear rate of convergence.

We develop a technique that allows us to prove convergence rates for (L₀, L₁)-smooth functions without assuming uniform bounds on the noise support. The key innovation behind our results is a carefully constructed stopping time. This is simultaneously large on average and allows us to decorrelate the adaptive stepsizes from the gradients, which is a major challenge in many analyses.

While Optimal Transport (OT) cost serves as the loss for popular generative models, we demonstrate that the OT map can be used as the generative model itself. Previous analogous approaches consider OT maps as generative models only in the latent spaces due to their poor performance in the original high-dimensional ambient space. In contrast, we fit OT maps directly in the ambient space, e.g., a space of high-dimensional images.

A major concern of Sliced Wasserstein (SW) distance is that it requires a large number of projections in high-dimensional settings. To address this concern, we derive projections from a small number of bottleneck projections. We introduce Hierachical Radon Transform (HRT) that recursively applies Radon Transform (RT). We design Hierarchical Sliced Wasserstein (HSW) distance to estimate the discrepancy between measures in high dimensions.

First, we prove that GANs with content or identity losses learn optimal transport (OT) maps between source and target measures in super-resolution tasks. Second, we empirically demonstrate that these learned OT maps are biased and provide an OT solver to recover an unbiased OT map. It provides nearly state-of-the-art performance on the unpaired AIM19 benchmark without having to use content or identity losses.

In this paper, we intend to demystify an interesting phenomenon: adversarial interaction in GANs creates non-homogeneous equilibrium by inducing Turing instability in a Pseudo-Reaction-Diffusion (PRD) model. This is in contrast to supervised learning where the identical model achieves homogeneous equilibrium.

In this study, we observe that a system in which a generator and a discriminator adversarially interact with each other exhibits Turing-like patterns in the hidden layer and top layer of the generator.

Despite numerous attempts sought to provide empirical evidence of adversarial regularization outperforming sole supervision, the theoretical understanding of such phenomena remains elusive. In this study, we aim to resolve whether adversarial regularization indeed performs better than sole supervision at a fundamental level.

In this article, we devise a method, which we call ALERT, to tackle missing band reconstruction. The proposed method reconstructs missing band with the sole supervision of spectral and spatial priors.

This paper seeks to address synthesis of high resolution multi-spectral satellite imagery using adversarial learning. Guided by the discovery of attention mechanism, we regulate the process of band synthesis through spatio-spectral Laplacian attention.

In this study, we propose to parameterize action variables by matrices, and train our policy network using Monte-Carlo sampling. We study the implications of parametric action space in a model-free environment from theoretical and empirical perspective.

This paper describes the techniques developed to enhance the Phobos image from MCC multi-frame acquisitions using image rectification and topographic data. After incorporating these techniques, the final Phobos image appears more representative, spatially enhanced, and has normalized radiometry to study its surface features.

Results of over ninety trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.

Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.

Here, we propose a robust framework that offers the provision to incorporate illumination and rotation invariance in the standard Discriminative Correlation Filter (DCF) formulation. We also supervise the detection stage of DCF trackers by eliminating false positives in the convolution response map.

This paper discusses a novel approach to regress in temporal domain, based on weighted aggregation of distinctive visual features and feature prioritization with entropy estimation in a recursive fashion.

Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.

In this paper, we study the necessity to capture various physical constraints through motion consistency which has been demonstrated to improve accuracy, robustness and more importantly rotation adaptiveness.

The developed algorithm has been implemented to yield the physically significant chemiluminescence emission from hydroxyl radicals in flames from line-of-sight integrated images. The effectiveness of this algorithm is highlighted using exemplary OH chemiluminescence images captured from a standard swirl stabilized research burner.

The present embodiment proposes an efficient Fast Fourier Transform (FFT) based hyper-spectral image compression technique to store multiple acquisitions over same region of interest and thereby, improve Signal to Noise Ratio (SNR) of hyper-spectral images which usually have coarse spatial resolution.

In this chapter, we propose a robust framework that offers the provision to incorporate illumination and rotation invariance in the standard Discriminative Correlation Filter (DCF) formulation. We also supervise the detection stage of DCF trackers by eliminating false positives in the convolution response map.

This chapter discusses a novel approach to regress in the temporal domain, based on weighted aggregation of distinctive visual features and feature prioritization with entropy estimation in a recursive fashion.