Search Results for author:
Found 3 papers, 2 papers with code
Generative Active Learning for Image Synthesis Personalization
The primary challenge in conducting active learning on generative models lies in the open-ended nature of querying, which differs from the closed form of querying in discriminative models that typically target a single concept.
Compositional Inversion for Stable Diffusion Models
It stems from the fact that during inversion, the irrelevant semantics in the user images are also encoded, forcing the inverted concepts to occupy locations far from the core distribution in the embedding space.
Indicative Image Retrieval: Turning Blackbox Learning into Grey
In many applications, we need the matching evidence to be indicated rather than just have the ranked list (e. g., the locations of the target proteins/cells/lesions in medical images).
Cannot find the paper you are looking for? You can
Submit a new open access paper.
