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:
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 present a representation for describing transition models in complex uncertain domains using relational rules.   For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state.   An iterative greedy algorithm is used to construct a set of deictic references that determine which objects are relevant in any given state.    Feed-forward neural networks are used to learn the transition distribution on the relevant objects' properties.   This strategy is demonstrated to be both more versatile and more sample efficient than learning a monolithic transition model in a simulated domain in which a robot pushes stacks of objects on a cluttered table.

Ex Output:
A new approach that learns a representation for describing transition models in complex uncertaindomains using relational rules. 


Ex Input:
This paper introduces an information theoretic co-training objective for unsupervised learning.  We consider the problem of predicting the future . Rather than predict future sensations (image pixels or sound waves) we predict ``hypotheses'' to be confirmed by future sensations . More formally, we assume a population distribution on pairs $(x,y)$ where we can think of $x$ as a past sensation and $y$ as a future sensation . We train both a predictor model $P_\Phi(z|x)$ and a confirmation model $P_\Psi(z|y)$ where we view $z$ as hypotheses (when predicted) or facts (when confirmed ). For a population distribution on pairs $(x,y)$ we focus on the problem of measuring the mutual information between $x$ and $ y$. By the data processing inequality this mutual information is at least as large as the mutual information between $x$ and $z$ under the distribution on triples $(x,z,y)$ defined by the confirmation model $P_\Psi(z|y)$. The information theoretic training objective for $P_\Phi(z|x)$ and $P_\Psi(z|y)$ can be viewed as a form of co-training where we want the prediction from $x$ to match the confirmation from $y$. We give experiments on applications to learning phonetics on the TIMIT dataset.

Ex Output:

Presents an information theoretic training objective for co-training and demonstrates its power in unsupervised learning of phonetics.