Tiny Synthetic Datasets, Big Results for Vision Models

Kari Jaaskelainen

21 Nov 2025 — 1 min read

Tiny synthetic images, big model results

Training today’s vision systems usually starts from large, self-supervised backbones. This work shows you can replace huge real datasets with a handful of smartly crafted, synthetic images—without losing performance.

The trick, called Linear Gradient Matching, distills a small set of images so that, after a frozen feature extractor, they push a linear classifier’s gradients to mimic those from the real data.

Optimized for linear probes on top of pre-trained models (no training from scratch).
Beats real-image baselines with far fewer samples.
Generalizes across backbones: e.g., a CLIP probe trained on a dataset distilled using a DINO model performs competitively.
Shines on fine-grained classification.
Doubles as a lens into model behavior—revealing embedding-space similarity and sensitivity to spurious correlations.

Why it matters: faster experiments, lower compute and memory, and potential privacy benefits when sharing distilled datasets instead of raw data.

Paper by George Cazenavette, Antonio Torralba, and Vincent Sitzmann. More: https://arxiv.org/abs/2511.16674

Paper: https://arxiv.org/abs/2511.16674v1

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#AI #ComputerVision #DatasetDistillation #SelfSupervisedLearning #MachineLearning #DeepLearning #ModelInterpretability #CLIP #DINO

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Tiny synthetic images, big model results

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