Detailed Instructions: 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.
See one example below:
Problem: 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.
Explanation: The abstract focusses on designing an adaptive loss scaling method, hence the generated output is correct.

Problem: Theories in cognitive psychology postulate that humans use similarity as a basis
for object categorization. However, work in image classification generally as-
sumes disjoint and equally dissimilar classes to achieve super-human levels of
performance on certain datasets. In our work, we adapt notions of similarity using
weak labels over multiple hierarchical levels to boost classification performance.
Instead of pitting clustering directly against classification, we use a warm-start
based evaluation to explicitly provide value to a clustering representation by its
ability to aid classification. We evaluate on CIFAR10 and a fine-grained classifi-
cation dataset to show improvements in performance with the procedural addition
of intermediate losses and weak labels based on multiple hierarchy levels. Further-
more, we show that pretraining AlexNet on hierarchical weak labels in conjunc-
tion with intermediate losses outperforms a classification baseline by over 17% on
a subset of Birdsnap dataset. Finally, we show improvement over AlexNet trained
using ImageNet pre-trained weights as initializations which further supports our
claim of the importance of similarity.
Solution:
Cluster before you classify; using weak labels to improve classification