Teacher: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Teacher: Now, understand the problem? If you are still confused, see the following example:
Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs. MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training. Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages. We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter. We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods. We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point.
Solution: We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.
Reason: The abstract focusses on designing an adaptive loss scaling method, hence the generated output is correct.

Now, solve this instance: Watermarks have been used for various purposes. Recently, researchers started to look into using them for deep neural networks. Some works try to hide attack triggers on their adversarial samples when attacking neural networks and others want to watermark neural networks to prove their ownership against plagiarism. Implanting a backdoor watermark module into a neural network is getting more attention from the community. In this paper, we present a general purpose encoder-decoder joint training method, inspired by generative adversarial networks (GANs). Unlike GANs, however, our encoder and decoder neural networks cooperate to find the best watermarking scheme given data samples. In other words, we do not design any new watermarking strategy but our proposed two neural networks will find the best suited method on their own. After being trained, the decoder can be implanted into other neural networks to attack or protect them (see Appendix for their use cases and real implementations). To this end, the decoder should be very tiny in order not to incur any overhead when attached to other neural networks but at the same time provide very high decoding success rates, which is very challenging. Our joint training method successfully solves the problem and in our experiments maintain almost 100\% encoding-decoding success rates for multiple datasets with very little modifications on data samples to hide watermarks. We also present several real-world use cases in Appendix.
Student:
We propose a novel watermark encoder-decoder neural networks. They perform a cooperative game to define their own watermarking scheme. People do not need to design watermarking methods any more.