no code implementations • 31 Dec 2023 • Peihao Wang, Zhiwen Fan, Dejia Xu, Dilin Wang, Sreyas Mohan, Forrest Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra
In this paper, we reveal that the gradient estimation in score distillation is inherent to high variance.
no code implementations • 31 Dec 2023 • Peihao Wang, Dejia Xu, Zhiwen Fan, Dilin Wang, Sreyas Mohan, Forrest Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra
In this paper, we reveal that the existing score distillation-based text-to-3D generation frameworks degenerate to maximal likelihood seeking on each view independently and thus suffer from the mode collapse problem, manifesting as the Janus artifact in practice.
no code implementations • 12 May 2023 • Xinyu Gong, Sreyas Mohan, Naina Dhingra, Jean-Charles Bazin, Yilei Li, Zhangyang Wang, Rakesh Ranjan
In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG).
no code implementations • CVPR 2023 • Xinyu Gong, Sreyas Mohan, Naina Dhingra, Jean-Charles Bazin, Yilei Li, Zhangyang Wang, Rakesh Ranjan
In this paper, we study a novel problem in egocentric action recognition, which we term as "Multimodal Generalization" (MMG).
1 code implementation • 11 Oct 2022 • Adria Marcos-Morales, Matan Leibovich, Sreyas Mohan, Joshua Lawrence Vincent, Piyush Haluai, Mai Tan, Peter Crozier, Carlos Fernandez-Granda
In this work, we propose two novel metrics: the unsupervised mean squared error (MSE) and the unsupervised peak signal-to-noise ratio (PSNR), which are computed using only noisy data.
no code implementations • 21 Nov 2021 • Sheng Liu, Aakash Kaku, Weicheng Zhu, Matan Leibovich, Sreyas Mohan, Boyang Yu, Haoxiang Huang, Laure Zanna, Narges Razavian, Jonathan Niles-Weed, Carlos Fernandez-Granda
Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty.
1 code implementation • EMNLP 2021 • Ananya B. Sai, Tanay Dixit, Dev Yashpal Sheth, Sreyas Mohan, Mitesh M. Khapra
Natural Language Generation (NLG) evaluation is a multifaceted task requiring assessment of multiple desirable criteria, e. g., fluency, coherency, coverage, relevance, adequacy, overall quality, etc.
no code implementations • NeurIPS 2021 • Sreyas Mohan, Joshua L. Vincent, Ramon Manzorro, Peter A. Crozier, Eero P. Simoncelli, Carlos Fernandez-Granda
Deep convolutional neural networks (CNNs) for image denoising are usually trained on large datasets.
no code implementations • 19 Jan 2021 • Joshua L. Vincent, Ramon Manzorro, Sreyas Mohan, Binh Tang, Dev Y. Sheth, Eero P. Simoncelli, David S. Matteson, Carlos Fernandez-Granda, Peter A. Crozier
This shows that the network exploits global and local information in the noisy measurements, for example, by adapting its filtering approach when it encounters atomic-level defects at the nanoparticle surface.
Denoising Materials Science Image and Video Processing
1 code implementation • ICCV 2021 • Dev Yashpal Sheth, Sreyas Mohan, Joshua L. Vincent, Ramon Manzorro, Peter A. Crozier, Mitesh M. Khapra, Eero P. Simoncelli, Carlos Fernandez-Granda
This is advantageous because motion compensation is computationally expensive, and can be unreliable when the input data are noisy.
Ranked #5 on Video Denoising on Set8 sigma50
no code implementations • 24 Oct 2020 • Sreyas Mohan, Ramon Manzorro, Joshua L. Vincent, Binh Tang, Dev Yashpal Sheth, Eero P. Simoncelli, David S. Matteson, Peter A. Crozier, Carlos Fernandez-Granda
SBD outperforms existing techniques by a wide margin on a simulated benchmark dataset, as well as on real data.
no code implementations • 10 Feb 2020 • Aakash Kaku, Sreyas Mohan, Avinash Parnandi, Heidi Schambra, Carlos Fernandez-Granda
Extraneous variables are variables that are irrelevant for a certain task, but heavily affect the distribution of the available data.
1 code implementation • 4 Dec 2019 • Alejandra Duarte, Chaitra V. Hegde, Aakash Kaku, Sreyas Mohan, José G. Raya
We benchmark our model against a human expert test-retest segmentation and conclude that our model is superior for Patellar and Tibial cartilage using dice score as the comparison metric.
no code implementations • NeurIPS Workshop Deep_Invers 2019 • Zahra Kadkhodaie, Sreyas Mohan, Eero P. Simoncelli, Carlos Fernandez-Granda
Here, however, we show that bias terms used in most CNNs (additive constants, including those used for batch normalization) interfere with the interpretability of these networks, do not help performance, and in fact prevent generalization of performance to noise levels not including in the training data.
1 code implementation • ICLR 2020 • Sreyas Mohan, Zahra Kadkhodaie, Eero P. Simoncelli, Carlos Fernandez-Granda
In contrast, a bias-free architecture -- obtained by removing the constant terms in every layer of the network, including those used for batch normalization-- generalizes robustly across noise levels, while preserving state-of-the-art performance within the training range.
2 code implementations • NeurIPS 2019 • Gautier Izacard, Sreyas Mohan, Carlos Fernandez-Granda
Frequency estimation is a fundamental problem in signal processing, with applications in radar imaging, underwater acoustics, seismic imaging, and spectroscopy.
no code implementations • 1 Jun 2017 • Cengiz Pehlevan, Sreyas Mohan, Dmitri B. Chklovskii
Blind source separation, i. e. extraction of independent sources from a mixture, is an important problem for both artificial and natural signal processing.
no code implementations • 29 May 2017 • Prasan A Shedligeri, Sreyas Mohan, Kaushik Mitra
To address this drawback we propose a data driven approach for learning the optimal aperture pattern to recover depth map from a single coded image.