Introduces VICIS task and training framework for inferring visual concepts from image sets, with experiments showing better accuracy, diversity, and generalization than standard VLMs on synthetic and ImageNet data.
Sliderspace: Decomposing the visual capabilities of diffusion models
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STRIDE boosts diversity in one-step diffusion models by injecting PCA-aligned pink noise into transformer features while preserving text alignment and quality.
LatentGandr computes local principal components from neighborhood embeddings in generative model latent spaces and visualizes them as interactive grids to improve exploration over global slider methods.
citing papers explorer
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Show Me Examples: Inferring Visual Concepts from Image Sets
Introduces VICIS task and training framework for inferring visual concepts from image sets, with experiments showing better accuracy, diversity, and generalization than standard VLMs on synthetic and ImageNet data.
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STRIDE: Training-Free Diversity Guidance via PCA-Directed Feature Perturbation in Single-Step Diffusion Models
STRIDE boosts diversity in one-step diffusion models by injecting PCA-aligned pink noise into transformer features while preserving text alignment and quality.
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LatentGandr: Visual Exploration of Generative AI Latent Space via Local Embeddings
LatentGandr computes local principal components from neighborhood embeddings in generative model latent spaces and visualizes them as interactive grids to improve exploration over global slider methods.