SlowGuess/NeurIPS_2023_OCR huggingface dataset:
D.8 Combination with supervised contrastive learning
Our proposed context-shift augmentation (abbreviated as CSA) can be integrated with the supervised contrastive learning method BCL [39] to further improve the generalization. In Table 11, we report the experimental results. We conduct experiments on CIFAR100-LT with varying imbalance ratios, showing that CSA consistently boosts the performance of BCL.
Table 11: Accuracy (%) on CIFAR100-LT by integrating the proposed CSA into BCL
Imbalance Ratio	100	50	10
BCL	51.9	56.6	64.9
BCL w/ CSA	52.6	57.1	65.8
D.9 Computational cost analysis of context-shift augmentation.
The proposed context-shift augmentation is based on the widely-used dual-branch network. Moreover, a context-extracting module is designed to calculate Grad-CAM as well as obtain the contexts. The context-extracting module has a very small computational cost, as it only needs to apply the gradient backward once at the last layer of the network, and can be ignored compared to the global gradient backward for updating the whole model. Besides, it does not spend too much memory space to save the extracted contexts, since the size of the context bank is set equal to the mini-batch size. In Table 12, we report the training time cost per epoch of CIFAR100-LT using a single RTX3090, which also demonstrates that the proposed method does not lead to too much additional computational overhead.
Table 12: Training time cost per epoch on CIFAR100-LT.
w/ CE	w/ BCL
Single-Branch	2.04 s	4.76 s
Dual-Branch	2.38 s	4.85 s
Ours	2.98 s	5.10 s