Reviews: Positive-Unlabeled Compression on the Cloud

The paper targets the application of network compression using a cloud platform. Instead of uploading all the training data onto the platform, the paper suggests uploading a small portion of data as positive (P) data and use larger datasets already on the platform as unlabeled (U) data. After training a PU classifier, the classifier will be used to select more P data from the U data. And such selected data, together with the original data, are used in a knowledge distillation framework to compress the original network. The experimental results show that the compressed network's performance is close to the original deep neural network trained on all data, on three widely used datasets.