  We present a deep neural network Real/Bogus classifier that improves
classification performance in the Tomo-e Gozen transient survey by handling
label errors in the training data. In the wide-field, high-frequency transient
survey with Tomo-e Gozen, the performance of conventional convolutional neural
network classifier is not sufficient as about $10^6$ bogus detections appear
every night. In need of a better classifier, we have developed a new two-stage
training method. In this training method, label errors in the training data are
first detected by normal supervised learning classification, and then they are
unlabeled and used for training of semi-supervised learning. For actual
observed data, the classifier with this method achieves an area under the curve
(AUC) of 0.9998 and a false positive rate (FPR) of 0.0002 at true positive rate
(TPR) of 0.9. This training method saves relabeling effort by humans and works
better on training data with a high fraction of label errors. By implementing
the developed classifier in the Tomo-e Gozen pipeline, the number of transient
candidates was reduced to $\sim$40 objects per night, which is $\sim$1/130 of
the previous version, while maintaining the recovery rate of real transients.
This enables more efficient selection of targets for follow-up observations.
