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Class imbalance: what SynBalance shows
SynBalance (ICCV Workshops 2025) adds generated images for rare classes. On ImageNet-LT, accuracy on the rarest classes rises from 12.6% to 21.4%.
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ROSE: removing objects in video with 3D data
ROSE removes objects from video along with their shadows and reflections, trained on 16,678 rendered 3D pairs. See the numbers and what they don’t prove.
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DAViD from Microsoft: vision trained on synthetic only
Microsoft’s DAViD trains depth, normals and masks for people on 300K synthetic images. See the numbers, where it trails Sapiens-2B, and the limits.
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Ghost AI: what we know about Builder.ai and Amazon
The “ghost AI” story: what the press reported about Builder.ai and Amazon’s checkout, what was disputed, and what it teaches about transparency in AI.
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Geo4D: 4D reconstruction trained only on synthetic
Geo4D (ICCV 2025) reconstructs 4D scenes from monocular video, trained only on synthetic data. See the numbers on real video and where it struggles.
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Big tech and synthetic data: what the evidence says
Nvidia, Google and OpenAI all talk about synthetic data. What each source says, what is announcement and what is result, and what it means for vision.
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Synthetic data in quality inspection
In quality inspection, rare defects are the dataset problem. What an industry article says about synthetic data, and what still needs rigorous review.
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The real-data limit, according to Sutskever
At NeurIPS 2024, Sutskever said pre-training as we know it will end: there’s only one internet. What the talk says, and what it doesn’t say about vision.
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Infinigen: procedural generation of 3D worlds
Infinigen (Princeton, CVPR 2023) builds natural 3D scenes from rules alone, with no external assets. What the paper measured, what it didn’t, and the cost.
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LucidSim: generated images to train a robot
MIT CSAIL’s LucidSim (CoRL 2024) trained a quadruped robot in simulation only, using AI-generated images. See the results, real and simulated.
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