embelishIn the REA-Net we utilize the binary cross-entropy loss to minimize the discrepancy between the predicted output and ground-truth The training process involves a total loss which contains deco
The training process in the REA-Net involves minimizing the difference between the predicted output and the ground-truth using the binary cross-entropy loss. This loss is used to calculate a total loss, which includes the decoder loss and the joint loss of the multi-task learning module. The total loss is defined as follows:
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