instruction:
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.
question:
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%).
answer:
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


question:
Optimization algorithms for training deep models not only affects the convergence rate and stability of the training process, but are also highly related to the generalization performance of trained models. While adaptive algorithms, such as Adam and RMSprop, have shown better optimization performance than stochastic gradient descent (SGD) in many scenarios, they often lead to worse generalization performance than SGD, when used for training deep neural networks (DNNs). In this work, we identify two problems regarding the direction and step size for updating the weight vectors of hidden units, which may degrade the generalization performance of Adam. As a solution, we propose the normalized direction-preserving Adam (ND-Adam) algorithm, which controls the update direction and step size more precisely, and thus bridges the generalization gap between Adam and SGD. Following a similar rationale, we further improve the generalization performance in classification tasks by regularizing the softmax logits. By bridging the gap between SGD and Adam, we also shed some light on why certain optimization algorithms generalize better than others.
answer:
A tailored version of Adam for training DNNs, which bridges the generalization gap between Adam and SGD.


question:
We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task. The algorithm uses either a gumbel softmax or online k-means clustering to quantize the dense representations. Discretization enables the direct application of algorithms from the NLP community which require discrete inputs. Experiments show that BERT pre-training achieves a new state of the art on TIMIT phoneme classification and WSJ speech recognition.
answer:

Learn how to quantize speech signal and apply algorithms requiring discrete inputs to audio data such as BERT.