About the implementation of in the cumulative learning stage

[YTEP-ZHI]

I'm confused about the implementation of fusing features from two branches. In the combiner.py of your code, it seems that features from two branchs are mixed by concatenating. (Related codes are as follows)

        mixed_feature = 2 * torch.cat((l * feature_a, (1-l) * feature_b), dim=1)
        output = model(mixed_feature, classifier_flag=True)
        loss = l * criterion(output, label_a) + (1 - l) * criterion(output, label_b)

However, in the section 4.1 of your paper, it's said that the two features are integrated by element-wise addition.

Is the feature fusing conducted by concatenating or element-wise addition? Or am I misunderstanding anything?(Correct me if I'm wrong) Looking forward to your reply.