5 papers
Test-Time Canonicalization by Foundation Models for Robust Perception
Utkarsh Singhal, Ryan Feng, Stella X. Yu +1
Perception in the real world requires robustness to diverse viewing conditions. Existing approaches often rely on specialized architectures or training with predefined data augment…
How to guess a gradient
Utkarsh Singhal, Brian Cheung, Kartik Chandra +4
How much can you say about the gradient of a neural network without computing a loss or knowing the label? This may sound like a strange question: surely the answer is "very little…
Learning to Transform for Generalizable Instance-wise Invariance
Utkarsh Singhal, Carlos Esteves, Ameesh Makadia +1
Computer vision research has long aimed to build systems that are robust to spatial transformations found in natural data. Traditionally, this is done using data augmentation or ha…
Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models
Utkarsh Singhal, Stella X. Yu, Zackery Steck +2
Multi-spectral imagery is invaluable for remote sensing due to different spectral signatures exhibited by materials that often appear identical in greyscale and RGB imagery. Paired…
Co-domain Symmetry for Complex-Valued Deep Learning
Utkarsh Singhal, Yifei Xing, Stella X. Yu
We study complex-valued scaling as a type of symmetry natural and unique to complex-valued measurements and representations. Deep Complex Networks (DCN) extends real-valued algebra…