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Binarized Spectral Compressive Imaging

NeurIPS 2023 · Architectures and efficiency › RNNs and normalization · approach: Dataset condensation and flow estimation · 9 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace vanilla 1-bit convolution with BiSR-Conv (ablation)binarized base model · KAIST simulationPSNR (dB): 23.90 -> 27.80Helped
use the four binarized modules (BiDS, BiUS, BiFD, BiFU) instead of normal binarized modules (ablation)BiSRNet · KAIST simulationPSNR (dB): 27.80 -> 29.76Helped
add spectral redistribution (SR) to BiSR-Conv (ablation)Baseline-2 · KAIST simulationPSNR (dB): 27.68 -> 28.97Helped
add Tanh(alpha x) Sign approximation (ablation)Baseline-2 · KAIST simulationPSNR (dB): 27.68 -> 28.74Helped
combine SR and Tanh(alpha x) (ablation)Baseline-2 · KAIST simulationPSNR (dB): 27.68 -> 29.76Helped
replace Clip(x) with Tanh(alpha x) for backward Sign approximation (ablation)BiSRNet · KAIST simulationPSNR (dB): 28.97 -> 29.76Helped
replace Quad(x) with Tanh(alpha x) for backward Sign approximation (ablation)BiSRNet · KAIST simulationPSNR (dB): 29.02 -> 29.76Helped
binarize only the bottleneck vs full-precision model (ablation)base model · KAIST simulationPSNR (dB): 34.11 -> 33.80Hurt
binarize only the decoder vs full-precision model (ablation)base model · KAIST simulationPSNR (dB): 34.11 -> 33.03Hurt
BiSRNet vs best prior BNN BTM1-bit HSI reconstruction · KAIST simulationavg PSNR (dB): 27.21 -> 29.76Helped

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