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TOPO-Loss for continuity-preserving crack detection using deep learning

Publications associées (39)

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Chemical machine learning with kernels: The key impact of loss functions

Volkan Cevher, Sandip De, Junhong Lin, Van Quang Nguyen

Machine learning promises to accelerate materials discovery by allowing computational efficient property predictions from a small number of reference calculations. As a result, the literature spent a considerable effort in designing representations that ca ...

2018

Abstract Amplitude demodulation (AM) is a signal decomposition technique by which a signal can be decomposed to a product of two signals, i.e, a quickly varying carrier and a slowly varying modulator. In this work, the probabilistic amplitude demodulation ...

Idiap2016

Probabilistic Amplitude Demodulation Features in Speech Synthesis for Improving Prosody

Philip Neil Garner, Milos Cernak

Amplitude demodulation (AM) is a signal decomposition technique by which a signal can be decomposed to a product of two signals, i.e, a quickly varying carrier and a slowly varying modulator. In this work, the probabilistic amplitude demodulation (PAD) fea ...

2016

Probabilistic Amplitude Demodulation Features in Speech Synthesis for Improving Prosody

Philip Neil Garner, Milos Cernak

2016

Mitigating Slow Dynamics of Low-Cost Chemical Sensors for Mobile Air Quality Monitoring Sensor Networks

Alcherio Martinoli, Ali Marjovi, Adrian Arfire

The last decade has seen a growing interest in air quality monitoring using networks of wireless low-cost sensor platforms. One of the unifying characteristics of chemical sensors typically used in real-world deployments is their slow response time. While ...

2016

Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e.g. SIFT. In this paper we use Convolutional Neural Networks (CNNs) to learn discriminant patc ...

2015

SVR vs MLP for Phone Duration Modelling in HMM-based Speech Synthesis

Philip Neil Garner, Pierre-Edouard Jean Charles Honnet

In this paper we investigate external phone duration models (PDMs) for improving the quality of synthetic speech in hidden Markov model (HMM)-based speech synthesis. Support Vector Regression (SVR) and Multilayer Perceptron (MLP) were used for this task. S ...

Idiap2014

SVR vs MLP for Phone Duration Modelling in HMM-based Speech Synthesis

Philip Neil Garner, Pierre-Edouard Jean Charles Honnet

2014

Adaptive networks are suitable for decentralized inference tasks. Recent works have intensively studied distributed optimization problems in the case where the nodes have to estimate a single optimum parameter vector collaboratively. However, there are man ...

Institute of Electrical and Electronics Engineers2014

An application of Evolutionary Optimization Algorithms for Determining Concentration and Velocity Profiles in Sheet flows and Overlying Layers

David Andrew Barry, Roham Bakhtyar

The Particle Swarm Optimization (PSO) method and the Genetic Algorithm (GA) were used to derive formulas for determining the velocity and concentration profiles in sheet flows. Specifically, these evolutionary optimization algorithms were used in conjuncti ...

Asme-Amer Soc Mechanical Eng2012