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 Q]: The conversion of scanned documents to digital forms is performed using an Optical Character Recognition (OCR) software. This work focuses on improving the quality of scanned documents in order to improve the OCR output. We create an end-to-end document enhancement pipeline which takes in a set of noisy documents and produces clean ones. Deep neural network based denoising auto-encoders are trained to improve the OCR quality. We train a blind model that works on different noise levels of scanned text documents. Results are shown for blurring and watermark noise removal from noisy scanned documents.
[EX A]: We designed and tested a REDNET (ResNet Encoder-Decoder) with 8 skip connections to remove noise from documents, including blurring and watermarks, resulting in a high performance deep network for document image cleanup. 

[EX Q]: Reinforcement learning (RL) methods achieved major advances in multiple tasks surpassing human performance. However, most of RL strategies show a certain degree of weakness and may become computationally intractable when dealing with high-dimensional and non-stationary environments. In this paper, we build a meta-reinforcement learning (MRL) method embedding an adaptive neural network (NN) controller for efficient policy iteration in changing task conditions. Our main goal is to extend RL application to the challenging task of urban autonomous driving in CARLA simulator.
[EX A]: A meta-reinforcement learning approach embedding a neural network controller applied to autonomous driving with Carla simulator.

[EX Q]: Transferring representations from large-scale supervised tasks to downstream tasks have shown outstanding results in Machine Learning in both Computer Vision and natural language processing (NLP). One particular example can be sequence-to-sequence models for Machine Translation (Neural Machine Translation - NMT). It is because, once trained in a multilingual setup, NMT systems can translate between multiple languages and are also capable of performing zero-shot translation between unseen source-target pairs at test time. In this paper, we first investigate if we can extend the zero-shot transfer capability of multilingual NMT systems to cross-lingual NLP tasks (tasks other than MT, e.g. sentiment classification and natural language inference). We demonstrate a simple framework by reusing the encoder from a multilingual NMT system, a multilingual Encoder-Classifier, achieves remarkable zero-shot cross-lingual classification performance, almost out-of-the-box on three downstream benchmark tasks - Amazon Reviews, Stanford sentiment treebank (SST) and Stanford natural language inference (SNLI). In order to understand the underlying factors contributing to this finding, we conducted a series of analyses on the effect of the shared vocabulary, the training data type for NMT models, classifier complexity, encoder representation power, and model generalization on zero-shot performance. Our results provide strong evidence that the representations learned from multilingual NMT systems are widely applicable across languages and tasks, and the high, out-of-the-box classification performance is correlated with the generalization capability of such systems.
[EX A]:
Zero-shot cross-lingual transfer by using multilingual neural machine translation