Research map / Architectures and efficiency / RNNs and normalization
Fast Algorithm for Low-Rank Tensor Completion in Delay-Embedded Space
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the FFT-accelerated circulant MDT formulation instead of MDT-Tucker | Tensor completion · Video recovery, slice missing | computing time (sec): 3160 -> 4.27 | Helped |
| Use the FFT-accelerated circulant MDT formulation instead of MDT-Tucker | Tensor completion · Video recovery, 70% voxel missing | computing time (sec): 684 -> 6.52 | Helped |
| Use the FFT-accelerated circulant MDT formulation instead of MDT-Tucker | Tensor completion · MRI recovery, τ=8 | PSNR: 26.39 -> 27.87 | Helped |
| Increase the delay window size from τ=8 to τ=16 (ablation) | Proposed method · MRI | PSNR: 27.87 -> 28.62 | Helped |
| Increase the delay window size from τ=16 to τ=32 (ablation) | Proposed method · MRI | PSNR: 28.62 -> 28.97 | Helped |
| Increase the delay window size from τ=32 to τ=48 (ablation) | Proposed method · MRI | PSNR: 28.97 -> 29.04 | Helped |
| Increase the delay window size from τ=48 to τ=64 (ablation) | Proposed method · MRI | PSNR: 29.04 -> 29.00 | Hurt |
| Replace normal MDT with circulant MDT (ablation) | Proposed method · Images with non-periodic edges | reconstruction accuracy | Hurt |
| Use low-rank structure only on odd modes, omitting even-mode factors (ablation) | Proposed method · Extremely ill-posed recovery, e.g. 95% missing | reconstruction accuracy | Hurt |
Closest papers by meaning
- Missing Slice Recovery for Tensors Using a Low-Rank Model in Embedded Space (CVPR 2018 · similarity 0.89)
- Fast Tensor Completion via Approximate Richardson Iteration (ICML 2025 · similarity 0.88)
- Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations (ICML 2019 · similarity 0.88)
- Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm (AAAI 2017 · similarity 0.87)
- Preconditioned Riemannian Gradient Descent Algorithm for Low-Multilinear-Rank Tensor Completion (ICML 2025 · similarity 0.84)
- Coarse-To-Fine Tensor Trains for Compact Visual Representations (ICML 2024 · similarity 0.84)
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