arXiv Machine Learning June 2, 2026 By Unique Subedi, Ambuj Tewari

Is Zero-Shot Super-Resolution Possible in Operator Learning?

Read the original on arXiv Machine Learning →

arXiv:2606. 00296v1 Announce Type: cross Abstract: Neural operators are often reported to exhibit zero-shot super-resolution, a phenomenon in which a model trained on coarse grids produces accurate predictions on finer testing grids without additional retraining.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 2

Limits of Resolution Equivariance in Fourier Neural Operators

arXiv:2606. 00677v1 Announce Type: new Abstract: Fourier Neural Operators are often assumed to generalize across spatial resolutions, enabling training on a coarse grid and deployment on a finer grid.

By Alex Colagrande, Paul Caillon, Eva Feillet, Alexandre Allauzen

arXiv Machine Learning

Jul 7

Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures

arXiv:2404. 06294v2 Announce Type: replace-cross Abstract: Super-Resolution (SR) is a time-hallowed image processing problem that aims to improve the quality of a Low-Resolution (LR) sample up to the standard of its High-Resolution (HR) counterpart.

By Arkaprabha Basu, Kushal Bose, Sankha Subhra Mullick, Anish Chakrabarty, Swagatam Das

diffusion benchmarks safety

arXiv Machine Learning

Aug 24

Resolution-Consistent Greedy Neural Approximation on Infinite-Dimensional Spaces

arXiv:2608.20812v1 Announce Type: new Abstract: We develop constructive approximation and learning guarantees for shallow neural models with infinite-dimensional inputs observed through finitely many...

By Pablo M. Bern\'a, Antonio Falc\'o, Diego Mond\'ejar

arXiv Machine Learning

Aug 7

A neural operator view on U-Nets for inverse imaging problems

arXiv:2608. 05839v1 Announce Type: cross Abstract: Deep neural networks have shown great empirical success in the solution of a wide variety of ill-posed inverse problems in imaging.

By Alexander Auras, Martin Burger, Samira Kabri, Michael Moeller, Michael Schopf-Kuester

safety

arXiv Machine Learning

Aug 18

Iterative Refinement Diffusion for Super-Resolved Data Assimilation of Multiscale Physical Systems

arXiv:2608. 14744v1 Announce Type: new Abstract: Recovering high-resolution states from sparse, low-resolution observations is a central challenge in scientific machine learning and data assimilation.

By Mrigank Dhingra, Ramchandran Muthukumar, Rebecca Willett, Omer San

diffusion benchmarks

arXiv Machine Learning

Jun 18

Effects of sparsity and superposition on loss in simple autoencoders

arXiv:2606. 18538v1 Announce Type: new Abstract: One of the major difficulties in the mechanistic interpretability of neural networks is the occurrence of polysemanticity, which suggests that each neuron is typically responsible for multiple different tasks, impeding a clean interpretation of their function.

By Mriganka Basu Roy Chowdhury, Eric McLaughlin Weiner

efficiency safety

One email a morning, machine-written

Related stories

Limits of Resolution Equivariance in Fourier Neural Operators

Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures

Resolution-Consistent Greedy Neural Approximation on Infinite-Dimensional Spaces

A neural operator view on U-Nets for inverse imaging problems

Iterative Refinement Diffusion for Super-Resolved Data Assimilation of Multiscale Physical Systems

Effects of sparsity and superposition on loss in simple autoencoders