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cs.CV2024
All Seeds Are Not Equal: Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds
Shuangqi Li, Hieu Le, Jingyi Xu +1
Text-to-image diffusion models have demonstrated remarkable capability in generating realistic images from arbitrary text prompts. However, they often produce inconsistent results…
cs.LG2024
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…