Text to Image Translation using Cycle GAN

In the recent past, text-to-image translation was an active field of research. The ability of a network to know a sentence's context and to create a specific picture that represents the sentence demonstrates the model's ability to think more like humans. Common text--translation methods employ Generative Adversarial Networks to generate high-text-images, but the images produced do not always represent the meaning of the phrase provided to the model as input. Using a captioning network to caption generated images, we tackle this problem and exploit the gap between ground truth captions and generated captions to further enhance the network. We present detailed similarities between our system and the methods already in place. Text-to-Image synthesis is a difficult problem with plenty of space for progress despite the current state-of - the-art results. Synthesized images from current methods give the described image a rough sketch but do not capture the true essence of what the text describes. The re-penny achievement of Generative Adversarial Networks (GANs) demonstrates that they are a decent contender for the decision of design to move toward this issue.

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Deep Generative Adversarial Networks for Image-to-Image Translation: A Review

Symmetry ◽

10.3390/sym12101705 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1705

Author(s):

Aziz Alotaibi

Keyword(s):

Three Dimensional ◽

Super Resolution ◽

Generative Adversarial Networks ◽

Future Research ◽

Comprehensive Overview ◽

Adversarial Networks ◽

Current State ◽

Image Translation ◽

Translation Techniques ◽

Future Research Directions

Many image processing, computer graphics, and computer vision problems can be treated as image-to-image translation tasks. Such translation entails learning to map one visual representation of a given input to another representation. Image-to-image translation with generative adversarial networks (GANs) has been intensively studied and applied to various tasks, such as multimodal image-to-image translation, super-resolution translation, object transfiguration-related translation, etc. However, image-to-image translation techniques suffer from some problems, such as mode collapse, instability, and a lack of diversity. This article provides a comprehensive overview of image-to-image translation based on GAN algorithms and its variants. It also discusses and analyzes current state-of-the-art image-to-image translation techniques that are based on multimodal and multidomain representations. Finally, open issues and future research directions utilizing reinforcement learning and three-dimensional (3D) modal translation are summarized and discussed.

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Successive Image Generation from a Single Sentence

ITM Web of Conferences ◽

10.1051/itmconf/20214003017 ◽

2021 ◽

Vol 40 ◽

pp. 03017

Author(s):

Amogh Parab ◽

Ananya Malik ◽

Arish Damania ◽

Arnav Parekhji ◽

Pranit Bari

Keyword(s):

State Of The Art ◽

Image Synthesis ◽

Image Sequence ◽

Generative Adversarial Networks ◽

Image Generation ◽

Single Sentence ◽

Adversarial Networks ◽

Successive Image ◽

Diagrammatic Representations ◽

Transfer Of Information

Through various examples in history such as the early man’s carving on caves, dependence on diagrammatic representations, the immense popularity of comic books we have seen that vision has a higher reach in communication than written words. In this paper, we analyse and propose a new task of transfer of information from text to image synthesis. Through this paper we aim to generate a story from a single sentence and convert our generated story into a sequence of images. We plan to use state of the art technology to implement this task. With the advent of Generative Adversarial Networks text to image synthesis have found a new awakening. We plan to take this task a step further, in order to automate the entire process. Our system generates a multi-lined story given a single sentence using a deep neural network. This story is then fed into our networks of multiple stage GANs inorder to produce a photorealistic image sequence.

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Handwritten Signature Spoofing With Conditional Generative Adversarial Nets

10.4018/978-1-7998-7323-5.ch006 ◽

2022 ◽

pp. 98-110

Author(s):

Md Fazle Rabby ◽

Md Abdullah Al Momin ◽

Xiali Hei

Keyword(s):

Computer Vision ◽

Image Synthesis ◽

Research Topic ◽

Generative Adversarial Networks ◽

Adversarial Networks ◽

Image Translation ◽

Handwritten Signature ◽

Condition Vector

Generative adversarial networks have been a highly focused research topic in computer vision, especially in image synthesis and image-to-image translation. There are a lot of variations in generative nets, and different GANs are suitable for different applications. In this chapter, the authors investigated conditional generative adversarial networks to generate fake images, such as handwritten signatures. The authors demonstrated an implementation of conditional generative adversarial networks, which can generate fake handwritten signatures according to a condition vector tailored by humans.

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Unpaired Image- to- Image Translation using Cycle Generative Adversarial Networks

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f1525.089620 ◽

2020 ◽

Vol 9 (6) ◽

pp. 380-385

Keyword(s):

Mapping Function ◽

Image Synthesis ◽

Input Image ◽

General Purpose ◽

Training Dataset ◽

Generative Adversarial Networks ◽

Inverse Mapping ◽

Style Transfer ◽

Adversarial Networks ◽

Image Translation

In this burgeoning age and society where people are tending towards learning the benefits adversarial network we hereby benefiting the society tend to extend our research towards adversarial networks as a general-purpose solution to image-to-image translation problems. Image to image translation comes under the peripheral class of computer sciences extending our branch in the field of neural networks. We apprentice Generative adversarial networks as an optimum solution for generating Image to image translation where our motive is to learn a mapping between an input image(X) and an output image(Y) using a set of predefined pairs[4]. But it is not necessary that the paired dataset is provided to for our use and hence adversarial methods comes into existence. Further, we advance a method that is able to convert and recapture an image from a domain X to another domain Y in the absence of paired datasets. Our objective is to learn a mapping function G: A —B such that the mapping is able to distinguish the images of G(A) within the distribution of B using an adversarial loss.[1] Because this mapping is high biased, we introduce an inverse mapping function F B—A and introduce a cycle consistency loss[7]. Furthermore we wish to extend our research with various domains and involve them with neural style transfer, semantic image synthesis. Our essential commitment is to show that on a wide assortment of issues, conditional GANs produce sensible outcomes. This paper hence calls for the attention to the purpose of converting image X to image Y and we commit to the transfer learning of training dataset and optimising our code.You can find the source code for the same here.

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Hierarchical Modes Exploring in Generative Adversarial Networks

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6732 ◽

2020 ◽

Vol 34 (07) ◽

pp. 10981-10988

Author(s):

Mengxiao Hu ◽

Jinlong Li ◽

Maolin Hu ◽

Tao Hu

Keyword(s):

Image Synthesis ◽

Generative Adversarial Networks ◽

Image Generation ◽

Specific Level ◽

Regularization Term ◽

Adversarial Networks ◽

Image Translation ◽

Real Change ◽

Quantitative Results ◽

Conditional Information

In conditional Generative Adversarial Networks (cGANs), when two different initial noises are concatenated with the same conditional information, the distance between their outputs is relatively smaller, which makes minor modes likely to collapse into large modes. To prevent this happen, we proposed a hierarchical mode exploring method to alleviate mode collapse in cGANs by introducing a diversity measurement into the objective function as the regularization term. We also introduced the Expected Ratios of Expansion (ERE) into the regularization term, by minimizing the sum of differences between the real change of distance and ERE, we can control the diversity of generated images w.r.t specific-level features. We validated the proposed algorithm on four conditional image synthesis tasks including categorical generation, paired and un-paired image translation and text-to-image generation. Both qualitative and quantitative results show that the proposed method is effective in alleviating the mode collapse problem in cGANs, and can control the diversity of output images w.r.t specific-level features.

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FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/476 ◽

2020 ◽

Cited By ~ 5

Author(s):

Run Wang ◽

Felix Juefei-Xu ◽

Lei Ma ◽

Xiaofei Xie ◽

Yihao Huang ◽

...

Keyword(s):

State Of The Art ◽

Image Synthesis ◽

Generative Adversarial Networks ◽

Layer By Layer ◽

Activation Patterns ◽

Adversarial Networks ◽

Neuron Activation ◽

Novel Approach ◽

Layer Neuron ◽

Facial Images

In recent years, generative adversarial networks (GANs) and its variants have achieved unprecedented success in image synthesis. They are widely adopted in synthesizing facial images which brings potential security concerns to humans as the fakes spread and fuel the misinformation. However, robust detectors of these AI-synthesized fake faces are still in their infancy and are not ready to fully tackle this emerging challenge. In this work, we propose a novel approach, named FakeSpotter, based on monitoring neuron behaviors to spot AI-synthesized fake faces. The studies on neuron coverage and interactions have successfully shown that they can be served as testing criteria for deep learning systems, especially under the settings of being exposed to adversarial attacks. Here, we conjecture that monitoring neuron behavior can also serve as an asset in detecting fake faces since layer-by-layer neuron activation patterns may capture more subtle features that are important for the fake detector. Experimental results on detecting four types of fake faces synthesized with the state-of-the-art GANs and evading four perturbation attacks show the effectiveness and robustness of our approach.

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Review of Medical Image Synthesis using GAN Techniques

ITM Web of Conferences ◽

10.1051/itmconf/20213701005 ◽

2021 ◽

Vol 37 ◽

pp. 01005

Author(s):

M. Krithika alias Anbu Devi ◽

K. Suganthi

Keyword(s):

Medical Image ◽

Medical Image Analysis ◽

Image Synthesis ◽

Generative Adversarial Networks ◽

High Radiation ◽

Adversarial Networks ◽

Image Translation ◽

Tomography Image ◽

Computed Tomography Image ◽

Single Modality

Generative Adversarial Networks (GANs) is one of the vital efficient methods for generating a massive, high-quality artificial picture. For diagnosing particular diseases in a medical image, a general problem is that it is expensive, usage of high radiation dosage, and time-consuming to collect data. Hence GAN is a deep learning method that has been developed for the image to image translation, i.e. from low-resolution to highresolution image, for example generating Magnetic resonance image (MRI) from computed tomography image (CT) and 7T from 3T MRI which can be used to obtain multimodal datasets from single modality. In this review paper, different GAN architectures were discussed for medical image analysis.

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SAR TO OPTICAL IMAGE SYNTHESIS FOR CLOUD REMOVAL WITH GENERATIVE ADVERSARIAL NETWORKS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-1-5-2018 ◽

2018 ◽

Vol IV-1 ◽

pp. 5-11 ◽

Cited By ~ 6

Author(s):

J. D. Bermudez ◽

P. N. Happ ◽

D. A. B. Oliveira ◽

R. Q. Feitosa

Keyword(s):

Mapping Function ◽

Image Synthesis ◽

Generative Adversarial Networks ◽

Atmospheric Conditions ◽

Sar Images ◽

Active Sensors ◽

Adversarial Networks ◽

Optical Images ◽

Image Translation ◽

Cloud Removal

<p><strong>Abstract.</strong> Optical imagery is often affected by the presence of clouds. Aiming to reduce their effects, different reconstruction techniques have been proposed in the last years. A common alternative is to extract data from active sensors, like Synthetic Aperture Radar (SAR), because they are almost independent on the atmospheric conditions and solar illumination. On the other hand, SAR images are more complex to interpret than optical images requiring particular handling. Recently, Conditional Generative Adversarial Networks (cGANs) have been widely used in different image generation tasks presenting state-of-the-art results. One application of cGANs is learning a nonlinear mapping function from two images of different domains. In this work, we combine the fact that SAR images are hardly affected by clouds with the ability of cGANS for image translation in order to map optical images from SAR ones so as to recover regions that are covered by clouds. Experimental results indicate that the proposed solution achieves better classification accuracy than SAR based classification.</p>

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