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.

Data augmentation is a useful technique to enlarge the size of the training set and prevent overfitting for different machine learning tasks when training data is scarce. However, current data augmentation techniques rely heavily on human design and domain knowledge, and existing automated approaches are yet to fully exploit the latent features in the training dataset. In this paper we propose  \textit{Parallel Adaptive GAN Data Augmentation}(PAGANDA), where the training set adaptively enriches itself with sample images automatically constructed from Generative Adversarial Networks (GANs) trained in parallel. We demonstrate by experiments that our data augmentation strategy, with little model-specific considerations, can be easily adapted to cross-domain deep learning/machine learning tasks such as image classification and image inpainting, while significantly improving model performance in both tasks. Our source code and experimental details are available at \url{https://github.com/miaojiang1987/k-folder-data-augmentation-gan/}.
We present an automated adaptive data augmentation that works for multiple different tasks. 

Reasoning over text and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering.  Transducing text to logical forms which can be operated on is a brittle and error-prone process . Operating directly on text by jointly learning representations and transformations thereof by means of neural architectures that lack the ability to learn and exploit general rules can be very data-inefficient and not generalise correctly . These issues are addressed by Neural Theorem Provers (NTPs) (Rocktäschel & Riedel, 2017), neuro-symbolic systems based on a continuous relaxation of Prolog’s backward chaining algorithm, where symbolic unification between atoms is replaced by a differentiable operator computing the similarity between their embedding representations . In this paper, we first propose Neighbourhood-approximated Neural Theorem Provers (NaNTPs) consisting of two extensions toNTPs, namely a) a method for drastically reducing the previously prohibitive time and space complexity during inference and learning, and b) an attention mechanism for improving the rule learning process, deeming them usable on real-world datasets. Then, we propose a novel approach for jointly reasoning over KB facts and textual mentions, by jointly embedding them in a shared embedding space. The proposed method is able to extract rules and provide explanations—involving both textual patterns and KB relations—from large KBs and text corpora. We show that NaNTPs perform on par with NTPs at a fraction of a cost, and can achieve competitive link prediction results on challenging large-scale datasets, including WN18, WN18RR, and FB15k-237 (with and without textual mentions) while being able to provide explanations for each prediction and extract interpretable rules.
We scale Neural Theorem Provers to large datasets, improve the rule learning process, and extend it to jointly reason over text and Knowledge Bases.

During the last years, a remarkable breakthrough has been made in AI domain
 thanks to artificial deep neural networks that achieved a great success in many
 machine learning tasks in computer vision, natural language processing, speech
 recognition, malware detection and so on. However, they are highly vulnerable
 to easily crafted adversarial examples. Many investigations have pointed out this
 fact and different approaches have been proposed to generate attacks while adding
 a limited perturbation to the original data. The most robust known method so far
 is the so called C&W attack [1]. Nonetheless, a countermeasure known as fea-
 ture squeezing coupled with ensemble defense showed that most of these attacks
 can be destroyed [6]. In this paper, we present a new method we call Centered
 Initial Attack (CIA) whose advantage is twofold : first, it insures by construc-
 tion the maximum perturbation to be smaller than a threshold fixed beforehand,
 without the clipping process that degrades the quality of attacks. Second, it is
 robust against recently introduced defenses such as feature squeezing, JPEG en-
 coding and even against a voting ensemble of defenses. While its application is
 not limited to images, we illustrate this using five of the current best classifiers
 on ImageNet dataset among which two are adversarialy retrained on purpose to
 be robust against attacks. With a fixed maximum perturbation of only 1.5% on
 any pixel, around 80% of attacks (targeted) fool the voting ensemble defense and
 nearly 100% when the perturbation is only 6%. While this shows how it is difficult
 to defend against CIA attacks, the last section of the paper gives some guidelines
 to limit their impact.

 In this paper, a new method we call Centered Initial Attack (CIA) is provided. It insures by construction the maximum perturbation to be smaller than a threshold fixed beforehand, without the clipping process.