Mar 25, 2024 · We concern these two issues and propose a framework DomainFusion, which leverages LDM in both latent level and pixel level for DG classification.
To address these challenges, we propose DomainFusion to simultaneously achieve knowledge extraction in the latent space and augmentation in the pixel space of ...
Experimental results demonstrate that DomainFusion outperforms diffusion-based methods by a large margin and achieves SOTA performance on existing DG benchmark ...
Nov 17, 2024 · Latent Diffusion Models (LDMs) are powerful and potential tools for facilitating generation-based methods for domain generalization.
Nov 17, 2024 · Latent Diffusion Models (LDMs) are powerful and potential tools for facilitating generation-based methods for domain generalization.
In DomainFusion, we incorpo- rate latent diffusion models in both latent-level and pixel-level, as shown in Figure 1 and Figure 3. In latent level, we ...
Nov 19, 2024 · Domain Generalization (DG) aims to transfer knowledge learned from multiple source domains to unseen domains. One of the primary challenges ...
Dec 8, 2023 · Domain Generalization with Latent Diffusion Models ... domains are available and the goal is to generalize to an unseen target domain.
DomainFusion: Generalizing To Unseen Domains with Latent Diffusion Models Yuyang Huang, Yabo Chen Yuchen Liu, Xiaopeng Zhang, Wenrui Dai, Hongkai Xiong, Qi ...
Dec 8, 2023 · We propose a novel data augmentation named Cross Domain Generative Augmentation (CDGA) that replaces the pointwise kernel estimates in ERM with new density ...
Missing: Unseen | Show results with:Unseen