Research map / Image generation / Autoregressive and latent generation
From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add training on external unlabeled images (ablation) | Supervised fMRI-to-image reconstruction · fMRI on ImageNet | 2-way identification accuracy: 80.1% -> 83.2% | Helped |
| Add self-supervised training on unlabeled test-fMRI (ablation) | Supervised reconstruction with unlabeled-image training · fMRI on ImageNet | 2-way identification accuracy: 83.2% -> 85.3% | Helped |
| Exclude the target test-fMRI from unlabeled fMRI training (ablation) | Full method · fMRI on ImageNet | 2-way identification accuracy: 85.3% -> 84.1% | Hurt |
| Add random shifts to input images during Encoder training | Image-to-fMRI Encoder · fMRI-to-image reconstruction | reconstruction quality | Helped |
Closest papers by meaning
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- NSD-Imagery: A Benchmark Dataset for Extending fMRI Vision Decoding Methods to Mental Imagery (CVPR 2025 · similarity 0.86)
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- MindEye2: Shared-Subject Models Enable fMRI-To-Image With 1 Hour of Data (ICML 2024 · similarity 0.86)
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