% Generated by docs/paper/paper_data.py from ../training/data/m5_macros.tex
\begin{tabularx}{\linewidth}{@{}>{\raggedright\arraybackslash}X cccc@{}}
\toprule
encoder & Yale$\to$Yale & TCGA$\to$TCGA & TCGA$\to$Yale & Yale$\to$TCGA \\
\midrule
\multicolumn{5}{@{}l}{\emph{Released encoders}} \\
\quad Phikon (ViT-B/16, 86M) & 0.872 (0.014) & 0.679 (0.012) & 0.811 (0.014) & 0.662 (0.003) \\
\quad H-optimus-0 (ViT-g/14, 1.1B) & 0.897 (0.012) & 0.676 (0.017) & 0.805 (0.020) & 0.653 (0.010) \\
\quad Hibou-B (ViT-B/14, 86M) & 0.786 (0.025) & 0.626 (0.011) & 0.749 (0.027) & 0.637 (0.011) \\
\quad Midnight-12k (ViT-g/14, 1.1B) & 0.834 (0.018) & 0.623 (0.019) & 0.727 (0.029) & 0.638 (0.005) \\
\quad ResNet-50, ImageNet (26M) & 0.700 (0.022) & 0.545 (0.017) & 0.638 (0.022) & 0.568 (0.015) \\
\quad DINOv2 ViT-S/14, natural images (22M) & 0.676 (0.029) & 0.495 (0.016) & 0.589 (0.016) & 0.573 (0.009) \\
\multicolumn{5}{@{}l}{\emph{Our ViT-S/16 after each stage}} \\
\quad Stage 1: self-supervised & 0.761 (0.027) & 0.589 (0.020) & 0.589 (0.047) & 0.520 (0.006) \\
\quad Stage 2: distilled (step 25,000) & 0.858 (0.022) & 0.705 (0.009) & 0.762 (0.011) & 0.625 (0.007) \\
\quad Stage 3, JEPA round 1, seed 3 & 0.848 (0.015) & 0.689 (0.014) & 0.798 (0.011) & 0.642 (0.009) \\
\quad Stage 3, JEPA round 1, seed 4 & 0.851 (0.014) & 0.687 (0.019) & 0.792 (0.016) & 0.642 (0.008) \\
\quad Stage 3, JEPA round 1, seed 5 & 0.850 (0.012) & 0.687 (0.018) & 0.795 (0.014) & 0.643 (0.008) \\
\quad Stage 3, JEPA round 2 from seed 3 (final) & 0.844 (0.022) & 0.690 (0.012) & 0.812 (0.015) & 0.650 (0.008) \\
\quad Stage 3, JEPA round 2 from seed 4 & 0.847 (0.018) & 0.681 (0.016) & 0.812 (0.010) & 0.651 (0.006) \\
\quad Stage 3, JEPA round 2 from seed 5 & 0.853 (0.019) & 0.685 (0.013) & 0.810 (0.015) & 0.652 (0.007) \\
\bottomrule
\end{tabularx}