Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
Weight Space Representation Learning via Neural Field Adaptation
Zhuoqian Yang, Mathieu Salzmann, Sabine Süsstrunk
We investigate the potential of weights to serve as effective representations, focusing on neural fields. Our key insight is that constraining the optimization space through a pre-…
cs.LG2025
QT-DoG: Quantization-aware Training for Domain Generalization
Saqib Javed, Hieu Le, Mathieu Salzmann
A key challenge in Domain Generalization (DG) is preventing overfitting to source domains, which can be mitigated by finding flatter minima in the loss landscape. In this work, we…
cs.LG2024
Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density
Shuangqi Li, Chen Liu, Tong Zhang +3
We introduce an approach to bias deep generative models, such as GANs and diffusion models, towards generating data with either enhanced fidelity or increased diversity. Our approa…