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Update paper tables and figures from Wave 1 results
- Regenerate accuracy tables with CIFAR-adapted stem data - Update figures with new baseline accuracies - All artifacts reflect Wave 1 complete experiments - CIFAR-10: 96.07% FP32, 94.64% BitNet (1.43% gap) - CIFAR-100: 79.14% FP32, 74.93% BitNet (4.21% gap)
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‎paper/tmlr/figures/accuracy_comparison_basic.png‎

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‎paper/tmlr/figures/accuracy_delta_heatmap_basic.png‎

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‎paper/tmlr/tables/accuracy_basic.tex‎

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\toprule
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Model & Dataset & Std (mean) & Std (std) & Bit (mean) & Bit (std) \\
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\midrule
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convnext_tiny & cifar10 & 70.74 & 7.29 & 69.72 & 5.49 \\
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convnext_tiny & cifar100 & 39.49 & 4.83 & 39.20 & 2.97 \\
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convnext_tiny & tiny_imagenet & 35.65 & - & 27.34 & - \\
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efficientnet_b0 & cifar10 & 84.91 & 0.39 & 78.56 & 0.49 \\
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efficientnet_b0 & cifar100 & 56.92 & 0.36 & 46.19 & 1.44 \\
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efficientnet_b0 & tiny_imagenet & 50.25 & - & 41.69 & - \\
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mobilenetv2_100 & cifar10 & 84.63 & 0.48 & 67.57 & 10.77 \\
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mobilenetv2_100 & cifar100 & 56.10 & 0.20 & 35.55 & 5.40 \\
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mobilenetv2_100 & tiny_imagenet & 45.76 & - & 38.79 & - \\
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resnet18 & cifar10 & 88.88 & 0.08 & 85.40 & 0.51 \\
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resnet18 & cifar100 & 62.40 & 0.45 & 58.06 & 0.23 \\
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resnet18 & imagenet & 65.53 & 0.12 & 39.36 & 0.40 \\
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resnet18 & tiny_imagenet & 54.85 & 0.20 & 49.04 & 0.23 \\
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resnet50 & cifar10 & 90.42 & 0.04 & 86.23 & 0.57 \\
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resnet50 & cifar100 & 65.58 & 0.05 & 56.48 & 0.17 \\
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resnet50 & tiny_imagenet & 58.30 & 0.41 & 44.70 & 2.70 \\
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resnet18 & cifar10 & 96.07 & 0.15 & 94.64 & 0.20 \\
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resnet18 & cifar100 & 79.14 & 0.11 & 74.93 & 0.23 \\
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resnet18 & tiny_imagenet & 67.04 & 0.23 & 62.10 & 0.22 \\
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resnet50 & cifar10 & 96.39 & 0.17 & 95.63 & 0.19 \\
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resnet50 & cifar100 & 80.71 & 0.10 & 76.34 & 0.96 \\
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resnet50 & tiny_imagenet & 71.77 & 0.12 & 65.00 & 0.61 \\
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\bottomrule
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\end{tabular}
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\end{table}

‎paper/tmlr/tables/accuracy_full.tex‎

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\begin{table}[h]
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\centering
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\caption{Test accuracy (\%) for standard and 1.58-bit models (full augmentation)}
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\label{tab:accuracy_full}
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\begin{tabular}{llcccc}
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\toprule
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Model & Dataset & Std (mean) & Std (std) & Bit (mean) & Bit (std) \\
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\midrule
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resnet18 & cifar10 & 90.10 & 0.12 & 86.20 & 0.29 \\
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resnet18 & cifar100 & 65.88 & 0.03 & 59.01 & 0.14 \\
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resnet50 & cifar10 & 92.06 & 0.11 & 85.77 & 0.60 \\
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resnet50 & cifar100 & 68.53 & 0.10 & 57.29 & 0.58 \\
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\bottomrule
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\end{tabular}
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\end{table}
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% Insufficient data: only [] version(s) available

‎paper/tmlr/tables/augmentation_ablation.tex‎

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\begin{table}[h]
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\centering
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\caption{Accuracy gap (FP32 - BitNet) across augmentation levels}
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\label{tab:augmentation_ablation}
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\begin{tabular}{llcccc}
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\toprule
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Model & Dataset & basic & randaug & cutout & full \\
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\midrule
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convnext_tiny & cifar10 & 1.02 & - & - & - \\
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convnext_tiny & cifar100 & 0.30 & - & - & - \\
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convnext_tiny & tiny_imagenet & 8.31 & - & - & - \\
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efficientnet_b0 & cifar10 & 6.35 & - & - & - \\
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efficientnet_b0 & cifar100 & 10.73 & - & - & - \\
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efficientnet_b0 & tiny_imagenet & 8.56 & - & - & - \\
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mobilenetv2_100 & cifar10 & 17.06 & - & - & - \\
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mobilenetv2_100 & cifar100 & 20.54 & - & - & - \\
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mobilenetv2_100 & tiny_imagenet & 6.97 & - & - & - \\
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resnet18 & cifar10 & 3.48 & 3.44 & 3.62 & 3.90 \\
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resnet18 & cifar100 & 4.34 & 5.47 & 4.34 & 6.88 \\
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resnet18 & imagenet & 26.16 & - & - & - \\
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resnet18 & tiny_imagenet & 5.81 & - & - & - \\
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resnet50 & cifar10 & 4.18 & 5.25 & 5.39 & 6.28 \\
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resnet50 & cifar100 & 9.11 & 10.38 & 9.05 & 11.25 \\
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resnet50 & tiny_imagenet & 13.60 & - & - & - \\
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\bottomrule
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\end{tabular}
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\end{table}
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% Insufficient augmentation data: only ['basic'] available

‎paper/tmlr/tables/layer_ablation.tex‎

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\toprule
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Model & Ablation & Accuracy (\%) & Gap Recovery \\
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\midrule
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resnet18 & Full BitNet & 85.40 & 0\% \\
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resnet18 & keep\_conv1 & 87.40 & 58\% \\
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resnet18 & keep\_layer1 & 86.06 & 19\% \\
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resnet18 & keep\_layer4 & 85.30 & -3\% \\
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resnet18 & keep\_fc & 85.50 & 3\% \\
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resnet18 & FP32 baseline & 88.88 & 100\% \\
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resnet18 & Full BitNet & 94.64 & 0\% \\
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resnet18 & FP32 baseline & 96.07 & 100\% \\
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\midrule
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efficientnet_b0 & Full BitNet & 78.56 & 0\% \\
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efficientnet_b0 & FP32 baseline & 84.91 & 100\% \\
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\midrule
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convnext_tiny & Full BitNet & 69.72 & 0\% \\
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convnext_tiny & FP32 baseline & 70.74 & 100\% \\
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\midrule
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mobilenetv2_100 & Full BitNet & 67.57 & 0\% \\
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mobilenetv2_100 & FP32 baseline & 84.63 & 100\% \\
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\midrule
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resnet50 & Full BitNet & 86.23 & 0\% \\
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resnet50 & keep\_conv1 & 88.47 & 54\% \\
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resnet50 & keep\_layer1 & 86.78 & 13\% \\
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resnet50 & keep\_layer4 & 85.81 & -10\% \\
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resnet50 & keep\_fc & 86.02 & -5\% \\
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resnet50 & FP32 baseline & 90.42 & 100\% \\
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resnet50 & Full BitNet & 95.63 & 0\% \\
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resnet50 & FP32 baseline & 96.39 & 100\% \\
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\bottomrule
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\end{tabular}
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\end{table}

‎paper/tmlr/tables/statistics.tex‎

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\begin{table}[h]
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\centering
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\caption{Statistical comparison of standard vs 1.58-bit models}
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\label{tab:statistics}
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\begin{tabular}{llcccc}
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\toprule
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Model & Dataset & $\Delta$ Acc & $t$ & $p$ & Cohen's $d$ \\
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\midrule
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resnet18 & tiny_imagenet & 5.81 & 23.66 & 0.002* & 33.24 \\
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efficientnet_b0 & cifar100 & 10.73 & 10.33 & 0.009* & 12.51 \\
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convnext_tiny & cifar100 & 0.30 & 0.15 & 0.886 & 0.08 \\
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resnet50 & cifar100 & 9.95 & 17.10 & 0.000* & 8.17 \\
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resnet50 & tiny_imagenet & 13.60 & 7.58 & 0.017* & 8.63 \\
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\bottomrule
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\multicolumn{6}{l}{\footnotesize * $p < 0.05$} \\
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\end{tabular}
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\end{table}
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% No valid statistical comparisons available

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