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Monday, 20 October 2025

A picture's worth a thousand (private) words: Hierarchical generation of coherent synthetic photo albums

Google Research 1 year ago 7

Researchers at Google developed a method to generate synthetic photo albums that maintain privacy through differential privacy while preserving thematic coherence across images. The approach uses a hierarchical text-based intermediary process where photos are first converted to captions and album summaries, these representations are privately fine-tuned using large language models, and then converted back to images via text-to-image generation. The method achieved high semantic similarity (measured by MAUVE scores) between real and synthetic albums when tested on the YFCC100M dataset of nearly 100 million Creative Commons images.

Teaching Gemini to spot exploding stars with just a few examples

Google Research 1 year ago 56

Researchers trained Google's Gemini model to classify astronomical events like supernovae by providing just 15 annotated examples per survey, rather than requiring millions of labeled images like traditional specialized models. The model achieved 93% accuracy across three major surveys (Pan-STARRS, MeerLICHT, and ATLAS) while generating human-readable explanations of its classifications and can assess its own uncertainty to flag uncertain cases. This approach enables transparent decision-making for astronomers processing millions of nightly alerts and can be rapidly adapted to new telescopes and scientific domains.

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