Joint Entity and Event Extraction with Generative Adversarial Imitation Learning

We propose a new framework for entity and event extraction based on generative adversarial imitation learning—an inverse reinforcement learning method using a generative adversarial network (GAN). We assume that instances and labels yield to various extents of difficulty and the gains and penalties (rewards) are expected to be diverse. We utilize discriminators to estimate proper rewards according to the difference between the labels committed by the ground-truth (expert) and the extractor (agent). Our experiments demonstrate that the proposed framework outperforms state-of-the-art methods.

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An Adversarial and Densely Dilated Network for Connectomes Segmentation

Symmetry ◽

10.3390/sym10100467 ◽

2018 ◽

Vol 10 (10) ◽

pp. 467 ◽

Cited By ~ 2

Author(s):

Ke Chen ◽

Dandan Zhu ◽

Jianwei Lu ◽

Ye Luo

Keyword(s):

State Of The Art ◽

Piriform Cortex ◽

Receptive Fields ◽

Ground Truth ◽

Training Data ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Boundary Map ◽

Segmentation Task ◽

The Brain

Automatic reconstructing of neural circuits in the brain is one of the most crucial studies in neuroscience. Connectomes segmentation plays an important role in reconstruction from electron microscopy (EM) images; however, it is rather challenging due to highly anisotropic shapes with inferior quality and various thickness. In our paper, we propose a novel connectomes segmentation framework called adversarial and densely dilated network (ADDN) to address these issues. ADDN is based on the conditional Generative Adversarial Network (cGAN) structure which is the latest advance in machine learning with power to generate images similar to the ground truth especially when the training data is limited. Specifically, we design densely dilated network (DDN) as the segmentor to allow a deeper architecture and larger receptive fields for more accurate segmentation. Discriminator is trained to distinguish generated segmentation from manual segmentation. During training, such adversarial loss function is optimized together with dice loss. Extensive experimental results demonstrate that our ADDN is effective for such connectomes segmentation task, helping to retrieve more accurate segmentation and attenuate the blurry effects of generated boundary map. Our method obtains state-of-the-art performance while requiring less computation on ISBI 2012 EM dataset and mouse piriform cortex dataset.

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Generative Model of Brain Microbleeds for MRI Detection of Vascular Marker of Neurodegenerative Diseases

Frontiers in Neuroscience ◽

10.3389/fnins.2021.778767 ◽

2021 ◽

Vol 15 ◽

Author(s):

Saba Momeni ◽

Amir Fazlollahi ◽

Leo Lebrat ◽

Paul Yates ◽

Christopher Rowe ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

State Of The Art ◽

Three Dimensional ◽

Ground Truth ◽

Imaging Features ◽

Neural Network Classifier ◽

Generative Adversarial Network ◽

Adversarial Network ◽

High Diversity

Cerebral microbleeds (CMB) are increasingly present with aging and can reveal vascular pathologies associated with neurodegeneration. Deep learning-based classifiers can detect and quantify CMB from MRI, such as susceptibility imaging, but are challenging to train because of the limited availability of ground truth and many confounding imaging features, such as vessels or infarcts. In this study, we present a novel generative adversarial network (GAN) that has been trained to generate three-dimensional lesions, conditioned by volume and location. This allows one to investigate CMB characteristics and create large training datasets for deep learning-based detectors. We demonstrate the benefit of this approach by achieving state-of-the-art CMB detection of real CMB using a convolutional neural network classifier trained on synthetic CMB. Moreover, we showed that our proposed 3D lesion GAN model can be applied on unseen dataset, with different MRI parameters and diseases, to generate synthetic lesions with high diversity and without needing laboriously marked ground truth.

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GANs for Semi-Supervised Opinion Spam Detection

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

10.24963/ijcai.2019/723 ◽

2019 ◽

Cited By ~ 1

Author(s):

Gray Stanton ◽

Athirai A. Irissappane

Keyword(s):

Text Classification ◽

State Of The Art ◽

Ground Truth ◽

Online Reviews ◽

Spam Detection ◽

Large Set ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Opinion Spam ◽

Source Of Information

Online reviews have become a vital source of information in purchasing a service (product). Opinion spammers manipulate reviews, affecting the overall perception of the service. A key challenge in detecting opinion spam is obtaining ground truth. Though there exists a large set of reviews, only a few of them have been labeled spam or non-spam. We propose spamGAN, a generative adversarial network which relies on limited labeled data as well as unlabeled data for opinion spam detection. spamGAN improves the state-of-the-art GAN based techniques for text classification. Experiments on TripAdvisor data show that spamGAN outperforms existing techniques when labeled data is limited. spamGAN can also generate reviews with reasonable perplexity.

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Disentangled generative adversarial network for low-dose CT

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-021-00749-z ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Wenchao Du ◽

Hu Chen ◽

Hongyu Yang ◽

Yi Zhang

Keyword(s):

Network Architecture ◽

Low Dose ◽

Noise Suppression ◽

State Of The Art ◽

Visual Quality ◽

Ct Images ◽

Generative Adversarial Network ◽

Low Dose Ct ◽

Adversarial Network ◽

Suppression Method

AbstractGenerative adversarial network (GAN) has been applied for low-dose CT images to predict normal-dose CT images. However, the undesired artifacts and details bring uncertainty to the clinical diagnosis. In order to improve the visual quality while suppressing the noise, in this paper, we mainly studied the two key components of deep learning based low-dose CT (LDCT) restoration models—network architecture and adversarial loss, and proposed a disentangled noise suppression method based on GAN (DNSGAN) for LDCT. Specifically, a generator network, which contains the noise suppression and structure recovery modules, is proposed. Furthermore, a multi-scaled relativistic adversarial loss is introduced to preserve the finer structures of generated images. Experiments on simulated and real LDCT datasets show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods.

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SGAN4AbSum: A Semantic-Enhanced Generative Adversarial Network for Abstractive Text Summarization

10.21203/rs.3.rs-648146/v1 ◽

2021 ◽

Author(s):

Tham Vo

Keyword(s):

Ground Truth ◽

Text Summarization ◽

Generative Adversarial Network ◽

Convolutional Network ◽

Training Strategy ◽

Adversarial Network ◽

Deep Recurrent Neural Network ◽

Benchmark Datasets ◽

Latent Representations ◽

Abstractive Summarization

Abstract In abstractive summarization task, most of proposed models adopt the deep recurrent neural network (RNN)-based encoder-decoder architecture to learn and generate meaningful summary for a given input document. However, most of recent RNN-based models always suffer the challenges related to the involvement of much capturing high-frequency/reparative phrases in long documents during the training process which leads to the outcome of trivial and generic summaries are generated. Moreover, the lack of thorough analysis on the sequential and long-range dependency relationships between words within different contexts while learning the textual representation also make the generated summaries unnatural and incoherent. To deal with these challenges, in this paper we proposed a novel semantic-enhanced generative adversarial network (GAN)-based approach for abstractive text summarization task, called as: SGAN4AbSum. We use an adversarial training strategy for our text summarization model in which train the generator and discriminator to simultaneously handle the summary generation and distinguishing the generated summary with the ground-truth one. The input of generator is the jointed rich-semantic and global structural latent representations of training documents which are achieved by applying a combined BERT and graph convolutional network (GCN) textual embedding mechanism. Extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed SGAN4AbSum which achieve the competitive ROUGE-based scores in comparing with state-of-the-art abstractive text summarization baselines.

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WHITE-BOX CARTOONIZATION USING AN EXTENDED GAN FRAMEWORK

International Journal of Engineering Applied Sciences and Technology ◽

10.33564/ijeast.2021.v05i12.049 ◽

2021 ◽

Vol 5 (12) ◽

Author(s):

Amey Thakur ◽

Hasan Rizvi ◽

Mega Satish

Keyword(s):

Smooth Surface ◽

Generative Models ◽

Surface Representation ◽

High Quality ◽

Generative Adversarial Network ◽

Structure Representation ◽

Texture Representation ◽

Adversarial Network ◽

Representation Structure ◽

New Framework

In the present study, we propose to implement a new framework for estimating generative models via an adversarial process to extend an existing GAN framework and develop a white-box controllable image cartoonization, which can generate high-quality cartooned images/videos from real-world photos and videos. The learning purposes of our system are based on three distinct representations: surface representation, structure representation, and texture representation. The surface representation refers to the smooth surface of the images. The structure representation relates to the sparse colour blocks and compresses generic content. The texture representation shows the texture, curves, and features in cartoon images. Generative Adversarial Network (GAN) framework decomposes the images into different representations and learns from them to generate cartoon images. This decomposition makes the framework more controllable and flexible which allows users to make changes based on the required output. This approach overcomes any previous system in terms of maintaining clarity, colours, textures, shapes of images yet showing the characteristics of cartoon images.

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Learning a Generative Model for Fusing Infrared and Visible Images via Conditional Generative Adversarial Network with Dual Discriminators

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

10.24963/ijcai.2019/549 ◽

2019 ◽

Cited By ~ 12

Author(s):

Han Xu ◽

Pengwei Liang ◽

Wei Yu ◽

Junjun Jiang ◽

Jiayi Ma

Keyword(s):

Probability Distribution ◽

State Of The Art ◽

Infrared Image ◽

Infrared Images ◽

Generative Adversarial Network ◽

Visible Image ◽

Qualitative And Quantitative ◽

Adversarial Network ◽

Fused Image ◽

Visible Images

In this paper, we propose a new end-to-end model, called dual-discriminator conditional generative adversarial network (DDcGAN), for fusing infrared and visible images of different resolutions. Unlike the pixel-level methods and existing deep learning-based methods, the fusion task is accomplished through the adversarial process between a generator and two discriminators, in addition to the specially designed content loss. The generator is trained to generate real-like fused images to fool discriminators. The two discriminators are trained to calculate the JS divergence between the probability distribution of downsampled fused images and infrared images, and the JS divergence between the probability distribution of gradients of fused images and gradients of visible images, respectively. Thus, the fused images can compensate for the features that are not constrained by the single content loss. Consequently, the prominence of thermal targets in the infrared image and the texture details in the visible image can be preserved or even enhanced in the fused image simultaneously. Moreover, by constraining and distinguishing between the downsampled fused image and the low-resolution infrared image, DDcGAN can be preferably applied to the fusion of different resolution images. Qualitative and quantitative experiments on publicly available datasets demonstrate the superiority of our method over the state-of-the-art.

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Deep inverse reinforcement learning for structural evolution of small molecules

Briefings in Bioinformatics ◽

10.1093/bib/bbaa364 ◽

2020 ◽

Author(s):

Brighter Agyemang ◽

Wei-Ping Wu ◽

Daniel Addo ◽

Michael Y Kpiebaareh ◽

Ebenezer Nanor ◽

...

Keyword(s):

Reinforcement Learning ◽

High Throughput Screening ◽

Structural Evolution ◽

Search Space ◽

New Drugs ◽

Inverse Reinforcement Learning ◽

Generative Adversarial Network ◽

Entropy Maximization ◽

Reward Function ◽

Adversarial Network

Abstract The size and quality of chemical libraries to the drug discovery pipeline are crucial for developing new drugs or repurposing existing drugs. Existing techniques such as combinatorial organic synthesis and high-throughput screening usually make the process extraordinarily tough and complicated since the search space of synthetically feasible drugs is exorbitantly huge. While reinforcement learning has been mostly exploited in the literature for generating novel compounds, the requirement of designing a reward function that succinctly represents the learning objective could prove daunting in certain complex domains. Generative adversarial network-based methods also mostly discard the discriminator after training and could be hard to train. In this study, we propose a framework for training a compound generator and learn a transferable reward function based on the entropy maximization inverse reinforcement learning (IRL) paradigm. We show from our experiments that the IRL route offers a rational alternative for generating chemical compounds in domains where reward function engineering may be less appealing or impossible while data exhibiting the desired objective is readily available.

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Synthesizing Depth Hand Images with GANs and Style Transfer for Hand Pose Estimation

Sensors ◽

10.3390/s19132919 ◽

2019 ◽

Vol 19 (13) ◽

pp. 2919 ◽

Cited By ~ 2

Author(s):

Wangyong He ◽

Zhongzhao Xie ◽

Yongbo Li ◽

Xinmei Wang ◽

Wendi Cai

Keyword(s):

Pose Estimation ◽

Ground Truth ◽

Training Image ◽

Training Data ◽

Generative Adversarial Network ◽

Style Transfer ◽

Visual Appearance ◽

Hand Pose Estimation ◽

Adversarial Network ◽

Hand Pose

Hand pose estimation is a critical technology of computer vision and human-computer interaction. Deep-learning methods require a considerable amount of tagged data. Accordingly, numerous labeled training data are required. This paper aims to generate depth hand images. Given a ground-truth 3D hand pose, the developed method can generate depth hand images. To be specific, a ground truth can be 3D hand poses with the hand structure contained, while the synthesized image has an identical size to that of the training image and a similar visual appearance to the training set. The developed method, inspired by the progress in the generative adversarial network (GAN) and image-style transfer, helps model the latent statistical relationship between the ground-truth hand pose and the corresponding depth hand image. The images synthesized using the developed method are demonstrated to be feasible for enhancing performance. On public hand pose datasets (NYU, MSRA, ICVL), comprehensive experiments prove that the developed method outperforms the existing works.

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Insulator Segmentation for Power Line Inspection Based on Modified Conditional Generative Adversarial Network

Journal of Sensors ◽

10.1155/2019/4245329 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8

Author(s):

Zishu Gao ◽

Guodong Yang ◽

En Li ◽

Tianyu Shen ◽

Zhe Wang ◽

...

Keyword(s):

Transmission Lines ◽

Power Supply ◽

Power Transmission ◽

State Of The Art ◽

Power Line ◽

Power Transmission Lines ◽

Generative Adversarial Network ◽

Convolutional Network ◽

Adversarial Network ◽

Feature Information

There are a large number of insulators on the transmission line, and insulator damage will have a major impact on power supply security. Image-based segmentation of the insulators in the power transmission lines is a premise and also a critical task for power line inspection. In this paper, a modified conditional generative adversarial network for insulator pixel-level segmentation is proposed. The generator is reconstructed by encoder-decoder layers with asymmetric convolution kernel which can simplify the network complexity and extract more kinds of feature information. The discriminator is composed of a fully convolutional network based on patchGAN and learns the loss to train the generator. It is verified in experiments that the proposed method has better performances on mIoU and computational efficiency than Pix2pix, SegNet, and other state-of-the-art networks.

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