Research map / Architectures and efficiency / Attention and transformers
Reduce Information Loss in Transformers for Pluralistic Image Inpainting
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use PUT50 rather than ICT50 | pluralistic image inpainting · ImageNet, mask ratio 40-60% | FID: 55.598 -> 45.153 | Helped |
| use PUTall rather than ICTall | pluralistic image inpainting · Places2, mask ratio 10-60% | FID: 23.331 -> 24.605 | Hurt |
| replace the non-overlapping patch encoder with a convolutional encoder (ablation) | PUT · FFHQ, mask ratio 20-40% | FID: 12.784 -> 163.610 | Hurt |
| use one codebook instead of separate masked and unmasked codebooks (ablation) | PUT · FFHQ, mask ratio 20-40% | FID: 12.784 -> 12.112 | Mixed |
| remove the decoder reference branch (ablation) | PUT · FFHQ, mask ratio 20-40% | FID: 12.784 -> 15.014 | Hurt |
| use quantized vectors rather than encoder features as transformer inputs (ablation) | PUT · FFHQ, mask ratio 20-40% | FID: 12.784 -> 22.824 | Hurt |
| remove random quantization of transformer input features during training (ablation) | PUT · FFHQ, mask ratio 20-40% | FID: 12.784 -> 43.621 | Hurt |
Closest papers by meaning
- FrameQuant: Flexible Low-Bit Quantization for Transformers (ICML 2024 · similarity 0.87)
- Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting (CVPR 2024 · similarity 0.86)
- An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels (ICLR 2025 · similarity 0.86)
- The Quantization Benefits of Residual-Free Transformers (NeurIPS 2026 · similarity 0.86)
- Outlier-aware Slicing for Post-Training Quantization in Vision Transformer (ICML 2024 · similarity 0.85)
- SyFormer: Structure-Guided Synergism Transformer for Large-Portion Image Inpainting (AAAI 2024 · similarity 0.85)
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