Research map / Image generation / Image restoration
Spatially-Adaptive Pixelwise Networks for Fast Image Translation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use spatially-varying pixelwise functions instead of a spatially uniform function (ablation) | ASAP-Net · Image translation | visual quality | Helped |
| Add sinusoidal positional encoding to pixelwise-function inputs (ablation) | ASAP-Net · Image translation from label maps | visual quality | Helped |
| Increase downsampling factor from S=32 to S=64 (ablation) | ASAP-Net · Facades 512×512 | visual quality: 14.2ms -> 12.1ms | Hurt |
| Decrease downsampling factor from S=32 to S=16 (ablation) | ASAP-Net · Facades 512×512 | visual quality: 14.2ms -> 28.1ms | Helped |
| Use very low-resolution image analysis to predict high-resolution pixelwise-function parameters | ASAP-Net · Cityscapes, small object classes | mIoU | Hurt |
Closest papers by meaning
- L2P: Unlocking Latent Potential for Pixel Generation (NeurIPS 2026 · similarity 0.87)
- Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers (ICML 2024 · similarity 0.87)
- PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion (NeurIPS 2026 · similarity 0.86)
- PixelART: Image-to-Layer Decomposition without Latents or Text-to-Image Pretraining (NeurIPS 2026 · similarity 0.85)
- Win-Win: Training High-Resolution Vision Transformers from Two Windows (ICLR 2024 · similarity 0.85)
- Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis (CVPR 2024 · similarity 0.85)
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