SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Table 17: The comparison of learning from easy samples and learning from difficult sample in our pipeline (APT-L2 (freeze)) on node classification. Micro F1 is reported in the table. (Under the setting of learning from easy samples, we replace  $\phi_{uncertain}$  with  $-\phi_{uncertain}$  in Eq.(4), and only sample instances with predictive uncertainty lower than  $T_{s}$ .)
Method\Dataset	brazil	dd242	dd68	dd687	wisconsin	cornell	cora	pubmed
Learning from easy samples	56.34(3.45)	14.38(0.53)	11.76(1.04)	9.90(0.64)	50.65(1.84)	48.09(1.72)	35.74(0.42)	46.03(0.17)
Learning from difficult samples (ours)	69.82(2.32)	16.79(0.88)	12.68(0.81)	10.34(1.12)	55.11(1.74)	48.76(2.20)	34.27(0.43)	46.21(0.15)
Time comparison: pre-training vs. training from scratch. Using a pre-trained model can significantly reduce the time required for training from scratch. The reason is that the weights of the pre-trained model have already been put close to appropriate and reasonable values; thus the model converges faster during fine-tuning on a test data. As shown in Figure 13, compared to regular GNN model (e.g. GIN), our model yields a speedup of  $4.7 \times$  on average (which is measured by the ratio of the training time of GIN to the fine-tuning time of APT). Based on above analysis, we can draw a conclusion that pre-training is beneficial both in effectiveness and efficiency.
Figure 13: The running time of our model and the basic GNN model on graph classification task. Our model achieves a speedup of  $4.7 \times$  on average compared with GIN.