To tackle the challenge of no direct guidance over the noise patterns, we implement a curriculum learning based training method to first train the model without considerations for noise.
we first focus on the loss from the prediction distribution p , and then take the noise modeling into account gradually along the training process, i.e., gradually increasing the importance of the loss from the observed distribution o while decreasing the importance of p. In this way, the prediction branch is roughly trained before the model managing to characterize the noise, thus avoids being stuck into poor local optimum.