Search Results for author: Uddeshya Upadhyay

Found 16 papers, 5 papers with code

ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models

1 code implementation ICCV 2023 Uddeshya Upadhyay, Shyamgopal Karthik, Massimiliano Mancini, Zeynep Akata

We propose ProbVLM, a probabilistic adapter that estimates probability distributions for the embeddings of pre-trained VLMs via inter/intra-modal alignment in a post-hoc manner without needing large-scale datasets or computing.

Active Learning Model Selection +1

USIM-DAL: Uncertainty-aware Statistical Image Modeling-based Dense Active Learning for Super-resolution

no code implementations27 May 2023 Vikrant Rangnekar, Uddeshya Upadhyay, Zeynep Akata, Biplab Banerjee

Dense regression is a widely used approach in computer vision for tasks such as image super-resolution, enhancement, depth estimation, etc.

Active Learning Depth Estimation +3

Likelihood Annealing: Fast Calibrated Uncertainty for Regression

no code implementations21 Feb 2023 Uddeshya Upadhyay, Jae Myung Kim, Cordelia Schmidt, Bernhard Schölkopf, Zeynep Akata

Recent advances in deep learning have shown that uncertainty estimation is becoming increasingly important in applications such as medical imaging, natural language processing, and autonomous systems.

Denoising Image Super-Resolution +2

Towards Automating Retinoscopy for Refractive Error Diagnosis

no code implementations10 Aug 2022 Aditya Aggarwal, Siddhartha Gairola, Uddeshya Upadhyay, Akshay P Vasishta, Diwakar Rao, Aditya Goyal, Kaushik Murali, Nipun Kwatra, Mohit Jain

We develop a video processing pipeline that takes retinoscopic videos as input and estimates the net refractive error based on our proposed extension of the retinoscopy mathematical model.


BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks

1 code implementation14 Jul 2022 Uddeshya Upadhyay, Shyamgopal Karthik, Yanbei Chen, Massimiliano Mancini, Zeynep Akata

Moreover, many of the high-performing deep learning models that are already trained and deployed are non-Bayesian in nature and do not provide uncertainty estimates.

Autonomous Driving Deblurring +2

The Manifold Hypothesis for Gradient-Based Explanations

no code implementations15 Jun 2022 Sebastian Bordt, Uddeshya Upadhyay, Zeynep Akata, Ulrike Von Luxburg

We propose a necessary criterion: their feature attributions need to be aligned with the tangent space of the data manifold.

Diabetic Retinopathy Detection

Robustness via Uncertainty-aware Cycle Consistency

1 code implementation NeurIPS 2021 Uddeshya Upadhyay, Yanbei Chen, Zeynep Akata

Unpaired image-to-image translation refers to learning inter-image-domain mapping without corresponding image pairs.

Autonomous Driving Image-to-Image Translation +1

Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement

no code implementations7 Oct 2021 Uddeshya Upadhyay, Viswanath P. Sudarshan, Suyash P. Awate

Our experiments with two different real-world datasets show that the proposed method (i)~is robust to OOD-noisy test data and provides improved accuracy and (ii)~quantifies voxel-level uncertainty in the predictions.

Image Enhancement Image-to-Image Translation +2

Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Data

no code implementations21 Jul 2021 Viswanath P. Sudarshan, Uddeshya Upadhyay, Gary F. Egan, Zhaolin Chen, Suyash P. Awate

Our sinogram-based uncertainty-aware DNN framework, namely, suDNN, estimates a standard-dose PET image using multimodal input in the form of (i) a low-dose/low-count PET image and (ii) the corresponding multi-contrast MRI images, leading to improved robustness of suDNN to OOD acquisitions.

Image-to-Image Translation

A Mixed-Supervision Multilevel GAN Framework for Image Quality Enhancement

no code implementations29 Jun 2021 Uddeshya Upadhyay, Suyash Awate

Deep neural networks for image quality enhancement typically need large quantities of highly-curated training data comprising pairs of low-quality images and their corresponding high-quality images.

Generative Adversarial Network Super-Resolution

Uncertainty-Guided Progressive GANs for Medical Image Translation

1 code implementation29 Jun 2021 Uddeshya Upadhyay, Yanbei Chen, Tobias Hepp, Sergios Gatidis, Zeynep Akata

However, the state-of-the-art GAN-based frameworks do not estimate the uncertainty in the predictions made by the network that is essential for making informed medical decisions and subsequent revision by medical experts and has recently been shown to improve the performance and interpretability of the model.

Denoising Image-to-Image Translation +2

Uncertainty-aware Generalized Adaptive CycleGAN

1 code implementation23 Feb 2021 Uddeshya Upadhyay, Yanbei Chen, Zeynep Akata

Unpaired image-to-image translation refers to learning inter-image-domain mapping in an unsupervised manner.

Image Denoising Image-to-Image Translation +1

Transformer Based Reinforcement Learning For Games

no code implementations9 Dec 2019 Uddeshya Upadhyay, Nikunj Shah, Sucheta Ravikanti, Mayanka Medhe

Recent times have witnessed sharp improvements in reinforcement learning tasks using deep reinforcement learning techniques like Deep Q Networks, Policy Gradients, Actor Critic methods which are based on deep learning based models and back-propagation of gradients to train such models.

reinforcement-learning Reinforcement Learning (RL)

Robust Super-Resolution GAN, with Manifold-based and Perception Loss

no code implementations16 Mar 2019 Uddeshya Upadhyay, Suyash P. Awate

Using loss functions that assume Gaussian-distributed residuals makes the learning sensitive to corruptions in clinical training sets.


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