The survey: Text generation models in deep learning

Iqbal, Touseef; Qureshi, Shaima

doi:10.1016/j.jksuci.2020.04.001

Cited by 119 publications

(77 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…AI can also be used to rewrite one article to suit several different channels or audience tastes. 20 A survey of recent deep learning methods for text generation by Iqbal and Qureshi (2020) concludes that text generated from images could be most amenable to GAN processing while topicto-text translation is likely to be dominated by variational autoencoders (VAE).…”

Section: Journalism and Text Generationmentioning

confidence: 99%

Artificial intelligence in the creative industries: a review

2021

View full text Add to dashboard Cite

This paper reviews the current state of the art in artificial intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically machine learning (ML) algorithms, is provided including convolutional neural networks (CNNs), generative adversarial networks (GANs), recurrent neural networks (RNNs) and deep Reinforcement Learning (DRL). We categorize creative applications into five groups, related to how AI technologies are used: (i) content creation, (ii) information analysis, (iii) content enhancement and post production workflows, (iv) information extraction and enhancement, and (v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, ML-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of ML in domains with fewer constraints, where AI is the ‘creator’, remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human-centric—where it is designed to augment, rather than replace, human creativity.

show abstract

Section: Journalism and Text Generationmentioning

confidence: 99%

Artificial intelligence in the creative industries: a review

2021

View full text Add to dashboard Cite

show abstract

“…It receives the image data components from the miner and generates the feature by using YOLO [22][23] algorithm. The general principles of image caption generator based on [24] it consist of two neural networks: YOLO based CNN , for feature extraction and LSTM [25] for generating the text sequence, which is similar to [15] model but instead of RNN [26], the LSTM was used because it carries relevant data during the training process and excludes non relevant information by forget gate. Figure 3 shows the Merge Architecture for Encoder-Decoder Model from [15].…”

Section: Image Classifiermentioning

confidence: 99%

Full Content-based Web Page Classification Methods by using Deep Neural Networks

Suleymanzade

Abdullayeva

2021

Stat., optim. inf. comput.

View full text Add to dashboard Cite

The quality of the web page classification process has a huge impact on information retrieval systems. In this paper, we proposed to combine the results of text and image data classifiers to get an accurate representation of the web pages. To get and analyse the data we created the complicated classifier system with data miner, text classifier, and aggregator. The process of image and text data classification has been achieved by the deep learning models. In order to represent the common view onto the web pages, we proposed three aggregation techniques that combine the data from the classifiers.

show abstract

“…However, it is notable that sentence-level OLHCCR has continuous strokes input of the target word at each time step of predicting besides the outputs of previous steps compared to text generation task [11]. Inspired by the above observation, we incorporate the prompting glyph information of the target word into each layer of the aforementioned autoregressive framework by adding the extra multi-modal fusion sub-layer, and the other sub-layers are identical as the structure of pre-trained autoregressive framework.…”

Section: Multi-layer Fusion Modulementioning

confidence: 99%

Sentence-level Online Handwritten Chinese Character Recognition

Li,

Yang,

Chen

et al. 2021

Preprint

View full text Add to dashboard Cite

Single online handwritten Chinese character recognition (single OLHCCR) has achieved prominent performance. However, in real application scenarios, users always write multiple Chinese characters to form one complete sentence and the contextual information within these characters holds the significant potential to improve the accuracy, robustness and efficiency of sentence-level OLHCCR. In this work, we first propose a simple and straightforward endto-end network, namely vanilla compositional network (VCN) to tackle the sentence-level OLHCCR. It couples convolutional neural network with sequence modeling architecture to exploit the handwritten character's previous contextual information. Although VCN performs much better than the state-of-the-art single OLHCCR model, it exposes high fragility when confronting with not well written characters such as sloppy writing, missing or broken strokes. To improve the robustness of sentence-level OLHCCR, we further propose a novel deep spatial-temporal fusion network (DSTFN). It utilizes a pre-trained autoregresssive framework as the backbone component, which projects each Chinese character into word embeddings, and integrates the spatial glyph features of handwritten characters and their contextual information multiple times at multi-layer fusion module. We also construct a large-scale sentencelevel handwriting dataset, named as CSOHD to evaluate models. Extensive experiment results demonstrate that DSTFN achieves the state-of-the-art performance, which presents strong robustness compared with VCN and exiting single OLHCCR models. The in-depth empirical analysis and case studies indicate that DSTFN can significantly improve the efficiency of handwriting input, with

show abstract

The survey: Text generation models in deep learning

Cited by 119 publications

References 29 publications

Artificial intelligence in the creative industries: a review

Artificial intelligence in the creative industries: a review

Full Content-based Web Page Classification Methods by using Deep Neural Networks

Sentence-level Online Handwritten Chinese Character Recognition

Contact Info

Product

Resources

About