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Vide-coded 64k demo system
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17 hours
fix: Use patch-based inference to match CNN training distribution
skal
17 hours
opt: Move invariant in1 calculation outside CNN convolution loops
skal
18 hours
opt: Vec4-optimize CNN convolution shaders for SIMD
skal
19 hours
chore: Update CNN architecture to 3×3×3 with new trained weights
skal
19 hours
docs: Update CNN training documentation with patch extraction
skal
19 hours
feat: Add salient-point patch extraction for CNN training
skal
20 hours
fix: Correct UV coordinate computation to match PyTorch linspace
skal
20 hours
fix: Add clamp to CNN final layer to match PyTorch training
skal
20 hours
refactor: Optimize CNN grayscale computation
skal
21 hours
update train_cnn.py and shader
skal
22 hours
feat: Add inference mode to train_cnn.py for ground truth generation
skal
22 hours
fix: CNN training normalization pipeline consistency
skal
23 hours
refactor: Optimize CNN normalization to eliminate redundant conversions
skal
24 hours
fix: Support variable kernel sizes in CNN layer generation
skal
25 hours
feat: CNN RGBD→grayscale with 7-channel augmented input
skal
25 hours
udpate
skal
29 hours
feat: Add multi-layer CNN support with framebuffer capture and blend control
skal
31 hours
docs: Update and streamline CNN training documentation
skal
31 hours
feat: Add checkpointing support to CNN training script
skal
31 hours
fix: Auto-expand single kernel size to all layers in training script
skal
31 hours
update target images
skal
31 hours
feat: Add coordinate-aware CNN layer 0 for position-dependent stylization
skal