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.

Let me give you an 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.
The answer to this example can be: We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.
Here is why: The abstract focusses on designing an adaptive loss scaling method, hence the generated output is correct.

OK. solve this:
The goal of generative models is to model the underlying data distribution of a
 sample based dataset. Our intuition is that an accurate model should in principle
 also include the sample based dataset as part of its induced probability distribution.
 To investigate this, we look at fully trained generative models using the Generative
 Adversarial Networks (GAN) framework and analyze the resulting generator
 on its ability to memorize the dataset. Further, we show that the size of the initial
 latent space is paramount to allow for an accurate reconstruction of the training
 data. This gives us a link to compression theory, where Autoencoders (AE) are
 used to lower bound the reconstruction capabilities of our generative model. Here,
 we observe similar results to the perception-distortion tradeoff (Blau & Michaeli
 (2018)). Given a small latent space, the AE produces low quality and the GAN
 produces high quality outputs from a perceptual viewpoint. In contrast, the distortion
 error is smaller for the AE. By increasing the dimensionality of the latent
 space the distortion decreases for both models, but the perceptual quality only
 increases for the AE.
Answer:
We analyze the impact of the latent space of fully trained generators by pseudo inverting them.