Search Results for author:
Found 22 papers, 9 papers with code
On Calibration and Conformal Prediction of Deep Classifiers
Our study suggests that it may be worthwhile to utilize adaptive CP methods, chosen for their enhanced conditional coverage, based on softmax values prior to (or after canceling) temperature scaling calibration.
Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned Guidance
An alternative for training a "task-specific" network for each observation model is to use pretrained deep denoisers for imposing only the signal's prior within iterative algorithms, without additional training.
Deep Internal Learning: Deep Learning from a Single Input
Yet, in many cases there is value in training a network just from the input at hand.
A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks
We start with an empirical study that shows that a decrease in within-class variability is also prevalent in the node-wise classification setting, however, not to the extent observed in the instance-wise case.
ADIR: Adaptive Diffusion for Image Reconstruction
In recent years, denoising diffusion models have demonstrated outstanding image generation performance.
Perturbation Analysis of Neural Collapse
In this paper, we propose a richer model that can capture this phenomenon by forcing the features to stay in the vicinity of a predefined features matrix (e. g., intermediate features).
Performance Analysis of Automotive SAR With Radar Based Motion Estimation
In this paper, we consider an automotive SAR system that produces SAR images of static objects based on ego vehicle velocity estimation from the radar return signal without the overhead in complexity and cost of using an auxiliary global navigation satellite system (GNSS) and inertial measurement unit (IMU).
Denoiser-based projections for 2-D super-resolution multi-reference alignment
We study the 2-D super-resolution multi-reference alignment (SR-MRA) problem: estimating an image from its down-sampled, circularly-translated, and noisy copies.
Extended Unconstrained Features Model for Exploring Deep Neural Collapse
Specifically, it has been shown that the learned features (the output of the penultimate layer) of within-class samples converge to their mean, and the means of different classes exhibit a certain tight frame structure, which is also aligned with the last layer's weights.
Direction of Arrival Estimation and Phase-Correction for Non-Coherent Sub-Arrays: A Convex Optimization Approach
Estimating the direction of arrival (DOA) of sources is an important problem in aerospace and vehicular communication, localization and radar.
Image Restoration by Deep Projected GSURE
In the first one, where no explicit prior is used, we show that the proposed approach outperforms other internal learning methods, such as DIP.
An Interpretation of Regularization by Denoising and its Application with the Back-Projected Fidelity Term
The vast majority of image recovery tasks are ill-posed problems.
Direction of Arrival Estimation for Non-Coherent Sub-Arrays via Joint Sparse and Low-Rank Signal Recovery
Estimating the directions of arrival (DOAs) of multiple sources from a single snapshot obtained by a coherent antenna array is a well-known problem, which can be addressed by sparse signal reconstruction methods, where the DOAs are estimated from the peaks of the recovered high-dimensional signal.
Kernel-Based Smoothness Analysis of Residual Networks
A major factor in the success of deep neural networks is the use of sophisticated architectures rather than the classical multilayer perceptron (MLP).
On the Convergence Rate of Projected Gradient Descent for a Back-Projection based Objective
Recently, several works have considered a back-projection (BP) based fidelity term as an alternative to the common least squares (LS), and demonstrated excellent results for popular inverse problems.
BP-DIP: A Backprojection based Deep Image Prior
Deep neural networks are a very powerful tool for many computer vision tasks, including image restoration, exhibiting state-of-the-art results.
Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers
For a known kernel, we design a closed-form correction filter that modifies the low-resolution image to match one which is obtained by another kernel (e. g. bicubic), and thus improves the results of existing pre-trained DNNs.
Back-Projection based Fidelity Term for Ill-Posed Linear Inverse Problems
This term encourages agreement between the projection of the optimization variable onto the row space of the linear operator and the pseudo-inverse of the linear operator ("back-projection") applied on the observations.
Image-Adaptive GAN based Reconstruction
In the recent years, there has been a significant improvement in the quality of samples produced by (deep) generative models such as variational auto-encoders and generative adversarial networks.
Performance Advantages of Deep Neural Networks for Angle of Arrival Estimation
The problem of estimating the number of sources and their angles of arrival from a single antenna array observation has been an active area of research in the signal processing community for the last few decades.
Super-Resolution via Image-Adapted Denoising CNNs: Incorporating External and Internal Learning
While deep neural networks exhibit state-of-the-art results in the task of image super-resolution (SR) with a fixed known acquisition process (e. g., a bicubic downscaling kernel), they experience a huge performance loss when the real observation model mismatches the one used in training.
Image Restoration by Iterative Denoising and Backward Projections
In this work, we propose an alternative method for solving inverse problems using off-the-shelf denoisers, which requires less parameter tuning.
