Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks

An instance with a bad mask might make a composite image that uses it look fake. This encourages us to learn segmentation by generating realistic composite images. To achieve this, we propose a novel framework that exploits a new proposed prior called the independence prior based on Generative Adversarial Networks (GANs). The generator produces an image with multiple category-specific instance providers, a layout module and a composition module. Firstly, each provider independently outputs a category-specific instance image with a soft mask. Then the provided instances’ poses are corrected by the layout module. Lastly, the composition module combines these instances into a final image. Training with adversarial loss and penalty for mask area, each provider learns a mask that is as small as possible but enough to cover a complete category-specific instance. Weakly supervised semantic segmentation methods widely use grouping cues modeling the association between image parts, which are either artificially designed or learned with costly segmentation labels or only modeled on local pairs. Unlike them, our method automatically models the dependence between any parts and learns instance segmentation. We apply our framework in two cases: (1) Foreground segmentation on category-specific images with box-level annotation. (2) Unsupervised learning of instance appearances and masks with only one image of homogeneous object cluster (HOC). We get appealing results in both tasks, which shows the independence prior is useful for instance segmentation and it is possible to unsupervisedly learn instance masks with only one image.

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Weakly supervised pneumonia localization in chest x‐rays using generative adversarial networks

Medical Physics ◽

10.1002/mp.15185 ◽

2021 ◽

Author(s):

Krishna Nand Keshavamurthy ◽

Carsten Eickhoff ◽

Krishna Juluru

Keyword(s):

Generative Adversarial Networks ◽

X Rays ◽

Adversarial Networks ◽

Weakly Supervised

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Road Detection From Remote Sensing Images by Generative Adversarial Networks

IEEE Access ◽

10.1109/access.2017.2773142 ◽

2018 ◽

Vol 6 ◽

pp. 25486-25494 ◽

Cited By ~ 20

Author(s):

Qian Shi ◽

Xiaoping Liu ◽

Xia Li

Keyword(s):

Remote Sensing ◽

Generative Adversarial Networks ◽

Road Detection ◽

Remote Sensing Images ◽

Adversarial Networks

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Weakly Supervised GAN for Image-to-Image Translation in the Wild

Mathematical Problems in Engineering ◽

10.1155/2020/6216048 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Zhiyi Cao ◽

Shaozhang Niu ◽

Jiwei Zhang

Keyword(s):

Generative Adversarial Networks ◽

Adversarial Networks ◽

Image Translation ◽

Latent Space ◽

In The Wild ◽

Adversarial Examples ◽

Weakly Supervised ◽

Public Datasets ◽

Global And Local ◽

The Given

Generative Adversarial Networks (GANs) have achieved significant success in unsupervised image-to-image translation between given categories (e.g., zebras to horses). Previous GANs models assume that the shared latent space between different categories will be captured from the given categories. Unfortunately, besides the well-designed datasets from given categories, many examples come from different wild categories (e.g., cats to dogs) holding special shapes and sizes (short for adversarial examples), so the shared latent space is troublesome to capture, and it will cause the collapse of these models. For this problem, we assume the shared latent space can be classified as global and local and design a weakly supervised Similar GANs (Sim-GAN) to capture the local shared latent space rather than the global shared latent space. For the well-designed datasets, the local shared latent space is close to the global shared latent space. For the wild datasets, we will get the local shared latent space to stop the model from collapse. Experiments on four public datasets show that our model significantly outperforms state-of-the-art baseline methods.

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