Model iteration with new data is important to improve generalization of the model. In general, there are two methods to deal with model incremental update: (a) retraining the model with merging all data together and (b) training a separate model with the new data based on transfer learning. However, the above methods are either time-consuming or suffering from over-fitting problems when the sample size of new data is small. To address this practical issue, we propose a new iteration model, the IterationNet, which can learn features of new data while maintain the performance on the old data. It is a new model iteration method based on knowledge distillation which adds consistency network and truncate L1 regularization. In classifying fake avatar images of Weibo users, IterationNet extremely decreased training time from 8 hours to 5 minutes while the accuracy rate is only reduced from 96% to 91% comparing to training with merged data. Compared with transfer learning, IterationNet showed increased accuracy rate by 21 percent with similar training time.
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