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Found 2 papers, 2 papers with code
Data Generation for Hardware-Friendly Post-Training Quantization
We analyze existing data generation methods based on batch normalization (BN) matching and identify several gaps between synthetic and real data: 1) Current generation algorithms do not optimize the entire synthetic dataset simultaneously; 2) Data augmentations applied during training are often overlooked; and 3) A distribution shift occurs in the final model layers due to the absence of BN in those layers.
GD-VDM: Generated Depth for better Diffusion-based Video Generation
GD-VDM is based on a two-phase generation process involving generating depth videos followed by a novel diffusion Vid2Vid model that generates a coherent real-world video.
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