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Learning a Unified Blind Image Quality Metric via On-Line and Off-Line Big Training Instances

Related publications (63)

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Federated Generative Privacy

Boi Faltings, Aleksei Triastcyn

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 private artificial data samples and empirica ...

IEEE COMPUTER SOC2020

Accurate and efficient interpretation of load-test data for asset-management

Sai Ganesh Sarvotham Pai

Increasing demand for new infrastructure and ageing of existing infrastructure has made management of infrastructure a key challenge of this century. Replacement of all ageing civil infrastructure is economically and environmentally unsustainable. Civil in ...

EPFL2019

Learning to Reduce Annotation Load

Ksenia Konyushkova

Modern machine learning methods and their applications in computer vision are known to crave for large amounts of training data to reach their full potential. Because training data is mostly obtained through humans who manually label samples, it induces a ...

EPFL2019

MMKG: Multi-modal Knowledge Graphs

Alberto Garcia Duran, Hongyu Li, Ye Liu

We present Mmkg, a collection of three knowledge graphs that contain both numerical features and (links to) images for all entities as well as entity alignments between pairs of KGs. Therefore, multi-relational link prediction and entity matching communiti ...

SPRINGER INTERNATIONAL PUBLISHING AG2019

Effort-driven Fact Checking

Thành Tâm Nguyên

The Web constitutes a valuable source of information. In recent years, it fostered the construction of large-scale knowledge bases, such as Freebase, YAGO, and DBpedia, each storing millions of facts about society in general, and specific domains, such as ...

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

Evaluating Quality of Screen Content Images Via Structural Variation Analysis

Daniel Thalmann

With the quick development and popularity of computers, computer-generated signals have drastically invaded into our daily lives. Screen content image is a typical example, since it also includes graphic and textual images as components as compared with na ...

IEEE COMPUTER SOC2018

Efﬁcient Feature Embeddings for Student Classiﬁcation with Variational Auto-encoders

Gathering labeled data in educational data mining (EDM) is a time and cost intensive task. However, the amount of available training data directly influences the quality of predictive models. Unlabeled data, on the other hand, is readily available in high ...

EDM2017

Linking Models and Experiments

Dominique Bonvin, Diogo Filipe Mateus Rodrigues, René Uwe Schneider

This position paper gives an overview of the discussion that took place at FIPSE 2 at Aldemar Resort, east of Heraklion, Crete, in June 21-23, 2014. This is the second conference in the series "Future Innovation in Process Systems Engineering" (http://fi-i ...

Amer Chemical Soc2016

Robust 3D Object Pose Estimation and Tracking from Monocular Images in Industrial Environments

Alberto Crivellaro

Recent advances in Computer Vision are changing our way of living and enabling new applications for both leisure and professional use. Regrettably, in many industrial domains the spread of state-of-the-art technologies is made challenging by the abundance ...

EPFL2016