Generative adversarial networks: introduction and outlook

Wang, Kunfeng; Gou, Chao; Duan, Yanjie; Lin, Yilun; Zheng, Xinhu; Wang, Fei‐Yue

doi:10.1109/jas.2017.7510583

Cited by 536 publications

(239 citation statements)

References 29 publications

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“…Although GANs allow us to introduce invariance and robustness of deep models with respect to not only affine transforms (e.g., rotation, scaling, or flipping) but also to some shape and appearance variations, convergence of the adversarial training and existence of its equilibrium point remain the open issues. Finally, there exist scenarios in which the generator renders multiple very similar examples which cannot improve the generalization of the system-it is known as the mode collapse problem (Wang et al, 2017).…”

Section: Data Augmentation By Generating Artificial Datamentioning

confidence: 99%

Data Augmentation for Brain-Tumor Segmentation: A Review

Nalepa

Marcinkiewicz²,

Kawulok

2019

Front. Comput. Neurosci.

245

127

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Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal role in scenarios in which the amount of high-quality ground-truth data is limited, and acquiring new examples is costly and time-consuming. This is a very common problem in medical image analysis, especially tumor delineation. In this paper, we review the current advances in data-augmentation techniques applied to magnetic resonance images of brain tumors. To better understand the practical aspects of such algorithms, we investigate the papers submitted to the Multimodal Brain Tumor Segmentation Challenge (BraTS 2018 edition), as the BraTS dataset became a standard benchmark for validating existent and emerging brain-tumor detection and segmentation techniques. We verify which data augmentation approaches were exploited and what was their impact on the abilities of underlying supervised learners. Finally, we highlight the most promising research directions to follow in order to synthesize high-quality artificial brain-tumor examples which can boost the generalization abilities of deep models.

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Section: Data Augmentation By Generating Artificial Datamentioning

confidence: 99%

Data Augmentation for Brain-Tumor Segmentation: A Review

Nalepa

Marcinkiewicz²,

Kawulok

2019

Front. Comput. Neurosci.

245

127

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“…GAN is first proposed in [9]. It was used to generate samples which distribution similar to real-world samples [18]. GAN achieves promising performance in image-to-image translation tasks [19][20][21][22] including super-resolution, semantic segmentation, image in-painting.…”

Section: Gan-based Workmentioning

confidence: 99%

Single Image De-Raining via Improved Generative Adversarial Nets

Ren

Nie

et al. 2020

Sensors

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Capturing images under rainy days degrades image visual quality and affects analysis tasks, such as object detection and classification. Therefore, image de-raining has attracted a lot of attention in recent years. In this paper, an improved generative adversarial network for single image de-raining is proposed. According to the principles of divide-and-conquer, we divide an image de-raining task into rain locating, rain removing, and detail refining sub-tasks. A multi-stream DenseNet, termed as Rain Estimation Network, is proposed to estimate the rain location map. A Generative Adversarial Network is proposed to remove the rain streaks. A Refinement Network is proposed to refine the details. These three models accomplish rain locating, rain removing, and detail refining sub-tasks, respectively. Experiments on two synthetic datasets and real world images demonstrate that the proposed method outperforms state-of-the-art de-raining studies in both objective and subjective measurements.

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“…Although GAN can perform well by itself, there have been numerous improvements and modifications [6] [7]. One prominent modification that has been done is called WGAN (Wasserstein GAN) [5].…”

Section: Wasserstein Ganmentioning

confidence: 99%

Audio Enhancement and Synthesis using Generative Adversarial Networks: A Survey

Torres-Reyes¹,

Latifi²

2019

IJCA

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Generative adversarial networks (GAN) have become prominent in the field of machine learning. Their premise is based on a minimax game in which a generator and discriminator "compete" against each other until an optimal point is reached. The goal of the generator is to produce synthetic samples that match that of real data. The discriminator tries to classify the real data as real and the generated data as not real. Together, the generator improves to the point where the fake data and real data are identical to the discriminator. GAN has been successfully applied in the image processing field over a large range of GAN variant architectures. Although not as prominent, the audio enhancement and synthesis field has also benefitted from GAN in a variety of different forms. In this survey paper, different techniques involving GAN will be explored relative to speech synthesis, speech enhancement, music generation, and general audio synthesis. Strengths and weaknesses of GAN will be looked at including variants created to combat those weaknesses. Also, a few similar machine learning architectures will be explored that may help achieve promising results.

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Generative adversarial networks: introduction and outlook

Cited by 536 publications

References 29 publications

Data Augmentation for Brain-Tumor Segmentation: A Review

Data Augmentation for Brain-Tumor Segmentation: A Review

Single Image De-Raining via Improved Generative Adversarial Nets

Audio Enhancement and Synthesis using Generative Adversarial Networks: A Survey

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