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.
One 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 is here: We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.
Explanation: The abstract focusses on designing an adaptive loss scaling method, hence the generated output is correct.

Now, solve this: The problem of visual metamerism is defined as finding a family of perceptually
indistinguishable, yet physically different images. In this paper, we propose our
NeuroFovea metamer model, a foveated generative model that is based on a mixture
of peripheral representations and style transfer forward-pass algorithms. Our
gradient-descent free model is parametrized by a foveated VGG19 encoder-decoder
which allows us to encode images in high dimensional space and interpolate
between the content and texture information with adaptive instance normalization
anywhere in the visual field. Our contributions include: 1) A framework for
computing metamers that resembles a noisy communication system via a foveated
feed-forward encoder-decoder network We observe that metamerism arises as a
byproduct of noisy perturbations that partially lie in the perceptual null space; 2)
A perceptual optimization scheme as a solution to the hyperparametric nature of
our metamer model that requires tuning of the image-texture tradeoff coefficients
everywhere in the visual field which are a consequence of internal noise; 3) An
ABX psychophysical evaluation of our metamers where we also find that the rate
of growth of the receptive fields in our model match V1 for reference metamers
and V2 between synthesized samples. Our model also renders metamers at roughly
a second, presenting a 1000 speed-up compared to the previous work, which now
allows for tractable data-driven metamer experiments.
Solution:
We introduce a novel feed-forward framework to generate visual metamers