Concept

Generative adversarial network

Related publications (533)

Understanding and Visualizing Raw Waveform-based CNNs

Modeling directly raw waveforms through neural networks for speech processing is gaining more and more attention. Despite its varied success, a question that remains is: what kind of information are such neural networks capturing or learning for different ...

2019

Non-filamentary non-volatile memory elements as synapses in neuromorphic systems

Yusuf Leblebici, Alessandro Fumarola, Severin Sidler

Crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing highly energy-efficient neuromorphic computing systems. For Deep Neural Networks (DNN), where information can be encoded as analog voltage and current levels, ...

IEEE2019

DeepFly3D, a deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila

Pascal Fua, Pavan P Ramdya, Helge Jochen Rhodin, Semih Günel, Daniel Eduardo Morales Garza, João Henrique Campagnolo

Studying how neural circuits orchestrate limbed behaviors requires the precise measurement of the positions of each appendage in 3-dimensional (3D) space. Deep neural networks can estimate 2-dimensional (2D) pose in freely behaving and tethered animals. Ho ...

2019

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition

Petr Motlicek, Subhadeep Dey

In this paper, we explore various approaches for semi- supervised learning in an end-to-end automatic speech recog- nition (ASR) framework. The first step in our approach in- volves training a seed model on the limited amount of labelled data. Additional u ...

2019

Federated Generative Privacy

Boi Faltings, Aleksei Triastcyn

In this paper, we propose FedGP, a framework for privacy-preserving data release in the federated learning setting. We use generative adversarial networks, generator components of which are trained by FedAvg algorithm, to draw privacy-preserving artificial ...

2019

Multi-agent reinforcement learning for adaptive demand response in smart cities

Jérôme Henri Kämpf

Buildings account for over 70% of the electricity use in the US. As cities grow, high peaks of electricity consumption are becoming more frequent, which leads to higher prices for electricity. Demand response is the coordination of electrical loads such th ...

IOP Publishing Ltd2019

Abstract Text Summarization: A Low Resource Challenge

Petr Motlicek

Text summarization is considered as a challenging task in the NLP community. The availability of datasets for the task of multilingual text summarization is rare, and such datasets are difficult to construct. In this work, we build an abstract text summari ...

Association for Computational Linguistics (ACL)2019

SparseFool: a few pixels make a big difference

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Apostolos Modas

Deep Neural Networks have achieved extraordinary results on image classification tasks, but have been shown to be vulnerable to attacks with carefully crafted perturbations of the input data. Although most attacks usually change values of many image's pixe ...

IEEE2019

Generating Artificial Data for Private Deep Learning

Boi Faltings, Aleksei Triastcyn

In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy for the original dataset. We use generative adversarial networks to draw privacy-preserving artificial data samples and d ...

2019

Inpainting in Omnidirectional Images for Privacy Protection

Touradj Ebrahimi, Evgeniy Upenik, Pinar Akyazi

Privacy protection is drawing more attention with the advances in image processing, visual and social media. Photo sharing is a popular activity, which also brings the concern of regulating permissions associated with shared content. This paper presents a ...

2019

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