Jun 15, 2023 · We evaluate attribution through "customization" methods, which tune an existing large-scale model toward a given exemplar object or style.
We evaluate attribution through "customization" methods, which tune an existing large-scale model toward a given exemplar object or style.
(b) Given the dataset, we can evaluate data attribution approaches by how high they rank the exemplar relative to other training images.
We evaluate attribution through customization methods, which tune an existing large-scale model toward a given exemplar object or style.
(b) Given the dataset, we can evaluate data attribution approaches by how high they rank the exemplar relative to other training images.
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Jun 13, 2024 · The goal of data attribution for text-to-image models is to identify the training images that most influence the generation of a new image.
The goal of data attribution for text-to-image models is to identify the training images that most influence the generation of a new image.
Nov 5, 2024 · The goal of data attribution for text-to-image models is to identify the training images that most influence the generation of a new image.
We evaluate attribution through customization methods, which tune an existing large-scale model toward a given exemplar object or style.
We show comparisons between pretrained features and features finetuned on our Object+Style attribution dataset. For object-centric models (top two rows), we ...