Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous response time. Therefore, top-performing deep models of high depth and complexity are not well suited for these applications with the limitations on the inference time. In order to get neural networks of better performance given the time limitations, we propose an unified framework that exploits a booster net to help train the lightweight net for prediction. We dub the whole process rocket launching, where the booster net is used to guide the learning of our light net throughout the whole training process. We analyze different loss functions aiming at pushing the light net to behave similarly to the booster net, and adopt the loss with best performance in our experiments. We use one technique called gradient block to improve the performance of light net and booster net further. Experiments on benchmark datasets and real-life industrial advertisement data show the effectiveness of our proposed method.
Rather than transferring a pre-trained teacher to a student, Zhou et al. propose a unified framework that exploits a booster net to help train the lightweight net for inference REF .
Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net
ArXiv
ArXiv
Mathematics Computer Science