SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Figure 7: Polynomial replacement training curves for (a) 6-STGCN-3-128 (b) 5-STGCN-3-128 (c) 4-STGCN-3-128 (d) 3-STGCN-3-128 (e) 2-STGCN-3-128 (f) 1-STGCN-3-128
(a)
Figure 8: Polynomial replacement training curves for (a) 12-STGCN-6-256 (b) 4-STGCN-6-256 (c) 2-STGCN-6-256
explains the enhanced performance of the STGCN-6-256 model, which features a reduced number of non-linear layers, as compared to the full-polynomial model baseline.
To substantiate our hypothesis, we plot the polynomial replacement training curve for the STGCN-6-256 model in Figure 8. The training curves for 12 effective non-linear layers (12-STGCN-6-256), four effective non-linear layers (4-STGCN-6-256), and two effective non-linear layers (2-STGCN-6-256) are presented. As the number of non-linear layers increases, the model achieves greater expressivity; however, the polynomial replacement process becomes increasingly unstable. Consequently, for the STGCN-6-256 model with only 4 non-linear layers, a more stable replacement process facilitates better convergence, ultimately leading to improved accuracy performance.