Few-shot Classifier GAN

Ali-Gombe, Adamu; Elyan, Eyad; Savoye, Yann; Jayne, Chrisina

doi:10.1109/ijcnn.2018.8489387

Cited by 25 publications

(17 citation statements)

References 13 publications

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“…The early version of this architecture, often called Vanilla GAN [35], uses the Kullback-Leibler divergence as the distribution similarity to produce false images. Some other architectures presented in the literature are C-GAN [36], S-GAN [37], AC-GAN [38], and F-SGAN [39], with an ever-growing market in the research community (Hindupuravinash, 'The GAN zoo', Github, hindupuravinash/the-gan-zoo: A list of all named GANs! (github.com)).…”

Section: Ganmentioning

confidence: 99%

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

et al. 2021

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Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks.

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Section: Ganmentioning

confidence: 99%

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

et al. 2021

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“…The early version of this architecture, often called Vanilla GAN [36], uses the Kullback-Leibler divergence as the distribution similarity to produce the false images. Some other architectures presented in the literature are C-GAN [37], S-GAN [38], AC-GAN [39] and F-SGAN [40], with an ever-growing market in the research community 7 Figure 13. GAN architecture [41] These networks are typically used to generate more data samples so that the data used to train classification systems can be improved.…”

Section: Ganmentioning

confidence: 99%

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Medina¹,

Espitia²,

Silva³

et al. 2021

Preprint

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Autonomous driving systems are increasingly becoming a necessary trend towards building smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that effectively considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper has reviewed various techniques towards proposing our own end-to-end autonomous vehicle system, considering the latest state on the art on computer vision, DSs, path planning, and parallelization.

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“…GANs have been successfully applied to different problems including image generation [48,49], segmentation and speech synthesis. In recent years they were also successfully applied to handle class-imbalance problems [50,51].…”

Section: Gan Modelsmentioning

confidence: 99%

Deep learning for symbols detection and classification in engineering drawings

2020

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Engineering drawings are commonly used in different industries such as Oil and Gas, construction, and other types of engineering. Digitising these drawings is becoming increasingly important. This is mainly due to the need to improve business practices such as inventory, assets management, risk analysis, and other types of applications. However, processing and analysing these drawings is a challenging task. A typical diagram often contains a large number of different types of symbols belonging to various classes and with very little variation among them. Another key challenge is the class-imbalance problem, where some types of symbols largely dominate the data while others are hardly represented in the dataset. In this paper, we propose methods to handle these two challenges. First, we propose an advanced bounding-box detection method for localising and recognising symbols in engineering diagrams. Our method is end-to-end with no user interaction. Thorough experiments on a large collection of diagrams from an industrial partner proved that our methods accurately recognise more than 94% of the symbols. Secondly, we present a method based on Deep Generative Adversarial Neural Network for handling class-imbalance.The proposed GAN model proved to be capable of learning from a small number of training examples. Experiment results showed that the proposed method greatly improved the classification of symbols in engineering drawings.

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Few-shot Classifier GAN

Cited by 25 publications

References 13 publications

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

Deep learning for symbols detection and classification in engineering drawings

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