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.

Ex Input:
Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure. The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections. In this paper, we find 99.9% of the gradient exchange in distributed SGD is redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth. To preserve accuracy during compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training. We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus. On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270x to 600x without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB. Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile.

Ex Output:
we find 99.9% of the gradient exchange in distributed SGD is redundant; we reduce the communication bandwidth by two orders of magnitude without losing accuracy.

Ex Input:
Effective training of neural networks requires much data. In the low-data regime,
parameters are underdetermined, and learnt networks generalise poorly. Data
Augmentation (Krizhevsky et al., 2012) alleviates this by using existing data
more effectively. However standard data augmentation produces only limited
plausible alternative data. Given there is potential to generate a much broader set
of augmentations, we design and train a generative model to do data augmentation.
The model, based on image conditional Generative Adversarial Networks, takes
data from a source domain and learns to take any data item and generalise it
to generate other within-class data items. As this generative process does not
depend on the classes themselves, it can be applied to novel unseen classes of data.
We show that a Data Augmentation Generative Adversarial Network (DAGAN)
augments standard vanilla classifiers well. We also show a DAGAN can enhance
few-shot learning systems such as Matching Networks. We demonstrate these
approaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and
VGG-Face data. In our experiments we can see over 13% increase in accuracy in
the low-data regime experiments in Omniglot (from 69% to 82%), EMNIST (73.9%
to 76%) and VGG-Face (4.5% to 12%); in Matching Networks for Omniglot we
observe an increase of 0.5% (from 96.9% to 97.4%) and an increase of 1.8% in
EMNIST (from 59.5% to 61.3%).

Ex Output:
Conditional GANs trained to generate data augmented samples of their conditional inputs used to enhance vanilla classification and one shot learning systems such as matching networks and pixel distance

Ex Input:
We recently observed that convolutional filters initialized
farthest apart from each other using offthe-
shelf pre-computed Grassmannian subspace
packing codebooks performed surprisingly well
across many datasets. Through this short paper,
we'd like to disseminate some initial results in this
regard in the hope that we stimulate the curiosity
of the deep-learning community towards considering
classical Grassmannian subspace packing
results as a source of new ideas for more efficient
initialization strategies.

Ex Output:
Initialize weights using off-the-shelf Grassmannian codebooks, get faster training and better accuracy