Publication

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

Publications associées (63)

Extensions of Peer Prediction Incentive Mechanisms

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

EPFL2024

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Boi Faltings, Ljubomir Rokvic, Panayiotis Danassis

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

2023

JPEG AIC-3 Dataset: Towards Defining the High Quality to Nearly Visually Lossless Quality Range

Touradj Ebrahimi, Michela Testolina, Davi Nachtigall Lazzarotto, Vlad Hosu

Visual data play a crucial role in modern society, and the rate at which images and videos are acquired, stored, and exchanged every day is rapidly increasing. Image compression is the key technology that enables storing and sharing of visual content in an ...

2023

Using raytraverse to render high accuracy images

Stephen William Wasilewski

Raytraverse is a python based software that helps to efficiently organize and guide the sampling of a lighting simulation within a scene. Radiance is embedded within Raytraverse to provide accurate and efficient solutions for each sampled ray. This talk wi ...

2023

incentive Mechanism Design for Responsible Data Governance: A Large-Scale Field Experiment

Boi Faltings, Naman Goel

A crucial building block of responsible artificial intelligence is responsible data governance, including data collection. Its importance is also underlined in the latest EU regulations. The data should be of high quality, foremost correct and representati ...

2023

Budget-Bounded Incentives for Federated Learning

Boi Faltings, Aris Filos Ratsikas, Adam Julian Richardson

We consider federated learning settings with independent, self-interested participants. As all contributions are made privately, participants may be tempted to free-ride and provide redundant or low-quality data while still enjoying the benefits of the FL ...

Springer Nature Switzerland AG 20202022

Performance Evaluation of Objective Image Quality Metrics on Conventional and Learning-Based Compression Artifacts

Touradj Ebrahimi, Evgeniy Upenik, Michela Testolina

Lossy image compression is a popular, simple and effective solution to reduce the amount of data representing digital pictures. In most lossy compression methods, the reduced volume of data in bits is achieved at the expense of introducing visual artifacts ...

2021

Cyber-Physical LPG Debutanizer Distillation Columns: Machine-Learning-Based Soft Sensors for Product Quality Monitoring

Jinzhi Lu

Refineries execute a series of interlinked processes, where the product of one unit serves as the input to another process. Potential failures within these processes affect the quality of the end products, operational efficiency, and revenue of the entire ...

2021

HISTOBREAST, a collection of brightfield microscopy images of Haematoxylin and Eosin stained breast tissue

Tiberiu Totu

Modern histopathology workflows rely on the digitization of histology slides. The quality of the resulting digital representations, in the form of histology slide image mosaics, depends on various specific acquisition conditions and on the image processing ...

2020

Towards a point cloud structural similarity metric

Touradj Ebrahimi, Evangelos Alexiou

Point cloud is a 3D image representation that has recently emerged as a viable approach for advanced content modality in modern communication systems. In view of its wide adoption, quality evaluation metrics are essential. In this paper, we propose and ass ...

2020

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search