A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Style Example-Guided Text Generation using Generative Adversarial Transformers
[article]
2020
arXiv
pre-print
Preserved Fulltext
The style encoder extracts a style code from the reference example, and the text decoder generates texts based on the style code and the context. ...
We introduce a language generative model framework for generating a styled paragraph based on a context sentence and a style reference example. ...
The proposed style example-guided text generation framework is based on the generative adversarial networks (GANs), and we utilize the transformer in both the generator and discriminator design. ...
arXiv:2003.00674v1
fatcat:2452647j5zhe7ouflakpolz5oe
Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
representation into a style-transferred text, (2) adversarial style transfer networks that use an adversarially trained generator to transform a latent representation in one style into a representation ...
In this paper, we propose a novel neural approach to unsupervised text style transfer, which we refer to as Cycle-consistent Adversarial autoEncoders (CAE) trained from non-parallel data. ...
We use generative adversarial networks (Goodfellow et al., 2014) to learn the two transformation functions. Let's consider the learning of the transformation T 1→2 . ...
arXiv:2010.00735v1
fatcat:wdvglp64mng5jcoozng7pn5koq
Separating Content from Style Using Adversarial Learning for Recognizing Text in the Wild
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2020 pre-print arXiv:2001.04189v2 fatcat:w4pwltuws5a2zcbgcugws6gawqOther Versions
Therefore, the discriminator can guide the generator according to the confusion of the recognizer, so that the generated patterns are clearer for recognition. ...
Benefiting from the character-level adversarial training, our framework requires only unpaired simple data for style supervision. ...
If a "G" is transformed to look more like a "C" and the recognizer predicts it to be a "C", the discriminator will learn that the pattern is a "C" and guide the generator to generate a clearer "G". ...
arXiv:2001.04189v3
fatcat:wpqllhdse5hitaauehg3ehriia
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives [Review Article]
2019
IEEE Computational Intelligence Magazine
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective ...
o ver the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. ...
The VoiceGAN framework consists of two generators/transformers (GAB and ) GBA and three discriminators ( , DA , DB and . ) Dstyle GAB attempts to transform instances from style A to style , B while GBA ...
doi:10.1109/mci.2019.2901088
fatcat:edkvfgy3ofgufcytngf5mktpae
DANCin SEQ2SEQ: Fooling Text Classifiers with Adversarial Text Example Generation
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Despite significant recent work on adversarial example generation targeting image classifiers, relatively little work exists exploring adversarial example generation for text classifiers; additionally, ...
In this work, we introduce DANCin SEQ2SEQ, a GAN-inspired algorithm for adversarial text example generation targeting largely black-box text classifiers. ...
Acknowledgments Many thanks to Will Monroe for his crackerjack adversarial text generation advice and expertise, and for sharing an alarming series of articles about 3D-printed turtles misclassified as ...
arXiv:1712.05419v1
fatcat:ccrkfg4nargw3hctkdi6iispym
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Can a generative model be trained to produce images from a specific domain, guided by a text prompt only, without seeing any image? In other words: can an image generator be trained "blindly"? ...
We show that through natural language prompts and a few minutes of training, our method can adapt a generator across a multitude of domains characterized by diverse styles and shapes. ...
Attempting to guide a facegenerator conversion with the text 'doctor', for example, causes the generator to produce mostly males, while using the text 'nurse' has the opposite effect. ...
arXiv:2108.00946v2
fatcat:lnn4ydsoenauxbpu6ijpm3ccn4
Exploring Controllable Text Generation Techniques
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Neural controllable text generation is an important area gaining attention due to its plethora of applications. ...
Although there is a large body of prior work in controllable text generation, there is no unifying theme. ...
In case of style transfer task, this loss is used to guide the generation process to output the target style tokens. ...
arXiv:2005.01822v2
fatcat:73tfkjvy7jcjdftlj4aurqswbu
Text Style Transfer: A Review and Experimental Evaluation
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Specifically, researchers have investigated the Text Style Transfer (TST) task, which aims to change the stylistic properties of the text while retaining its style independent content. ...
This article aims to provide a comprehensive review of recent research efforts on text style transfer. ...
There are two variants of the GST model: the Blind Generative Style Transformer (B-GST) and the Guided Generative Style Transformer (G-GST). ...
arXiv:2010.12742v2
fatcat:gmkjxf7f7jhivbo6mayaxjsk7q
A Review of Text Style Transfer using Deep Learning
2021
IEEE Transactions on Artificial Intelligence
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 pre-print arXiv:2109.15144v1 fatcat:qnksodu7jjeapo4shzq5f4q4ia
A systematic review of text style transfer methodologies using deep learning is presented in this paper. ...
The review is structured around two key stages in the text style transfer process, namely, representation learning and sentence generation in a new style. ...
[15] proposed two models, Blind Generative Style Transformer (B-GST) and Guided Generative Style Transformer (G-GST), that follow the same modeling approach as DeleteOnly and DeleteAndRetrieve [14] ...
doi:10.1109/tai.2021.3115992
fatcat:jn6trym6azdj7iomd2goyucbfi
Spatial Fusion GAN for Image Synthesis
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Recent advances in generative adversarial networks (GANs) have shown great potentials in realistic image synthesis whereas most existing works address synthesis realism in either appearance space or geometry ...
The appearance synthesizer adjusts the color, brightness and styles of the foreground objects and embeds them into background images harmoniously, where a guided filter is introduced for detail preserving ...
Take scene text image synthesis as an example. ...
arXiv:1812.05840v3
fatcat:uzent4sy35cpjkslziikyppuni
AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples
2018
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
First, we propose knowledge-guided adversarial example generators for incorporating large lexical resources in entailment models via only a handful of rule templates. ...
Second, to make the entailment model-a discriminator-more robust, we propose the first GAN-style approach for training it using a natural language example generator that iteratively adjusts based on the ...
For each mini-batch, we generate new entailment examples, Z G using our adversarial examples generator. ...
doi:10.18653/v1/p18-1225
dblp:conf/acl/HovyKSK18
fatcat:533izj4jenckphvdhmltq3i75u
Spatial Fusion GAN for Image Synthesis
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Recent advances in generative adversarial networks (GANs) have shown great potentials in realistic image synthesis whereas most existing works address synthesis realism in either appearance space or geometry ...
The appearance synthesizer adjusts the color, brightness and styles of the foreground objects and embeds them into background images harmoniously, where a guided filter is introduced for detail preserving ...
Take scene text image synthesis as an example. ...
doi:10.1109/cvpr.2019.00377
dblp:conf/cvpr/ZhanZL19
fatcat:mxijttgvlvfbfmuztb4nva2fpu
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective ...
Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. ...
EMOTION CONVERSION Emotion conversion is a specific style transformation task. ...
arXiv:1809.08927v1
fatcat:m5mencegljgsphub3p62ltrhby
Emotional Text Generation Based on Cross-Domain Sentiment Transfer
2019
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
By combining adversarial reinforcement learning with supervised learning, our model is able to extract patterns of sentiment transformation and apply them in emotional text generation. ...
Generative adversarial network (GAN) has shown promising results in natural language generation and data enhancement. ...
In addition, we will also apply this model to the data-to-text generation task. FIGURE 1 . 1 An example of text style transformation.
FIGURE 2 . 2 Overview of our approach. ...
doi:10.1109/access.2019.2931036
fatcat:mdsmfs37krc4nb2gcu6htjnmhy
AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
First, we propose knowledge-guided adversarial example generators for incorporating large lexical resources in entailment models via only a handful of rule templates. ...
Second, to make the entailment model - a discriminator - more robust, we propose the first GAN-style approach for training it using a natural language example generator that iteratively adjusts based on ...
For each mini-batch, we generate new entailment examples, Z G using our adversarial examples generator. ...
arXiv:1805.04680v1
fatcat:5bmbx4gbdrfjniicylvgr3bscq
« Previous
Showing results 1 — 15 out of 15,576 results