3 papers
cs.CV2026
End-to-End Optimization of Incoherent Imaging for Classification Under Detector-Limited Readout
Archer Wang, Joshua Chen, Sachin Vaidya +1
End-to-end co-optimization of optical front-ends (e.g. metasurfaces) and neural network back-ends has been widely applied to imaging tasks, yet a formalism characterizing when and…
cs.CV2026
Everything at Every Scale: Scale-Invariant Diffusion with Continuous Super-Resolution
Zixin Jessie Chen, Zhuo Chen, Archer Wang +4
Creating images from noise is image generation; reconstructing fine details from coarse inputs is super-resolution. Despite their practical differences, both can be understood as r…
cs.CV2026
Unsupervised Decomposition and Recombination with Discriminator-Driven Diffusion Models
Archer Wang, Emile Anand, Yilun Du +1
Decomposing complex data into factorized representations can reveal reusable components and enable synthesizing new samples via component recombination. We investigate this in the…