Concept

Transfer learning

Related publications (71)

Multimode optical fiber transmission with a deep learning network

Demetri Psaltis, Christophe Moser, Damien Claude-Marie Loterie, Babak Rahmani, Georgia Konstantinou

Multimode fibers (MMFs) are an example of a highly scattering medium, which scramble the coherent light propagating within them to produce seemingly random patterns. Thus, for applications such as imaging and image projection through an MMF, careful measur ...

CHINESE ACAD SCIENCES, CHANGCHUN INST OPTICS FINE MECHANICS AND PHYSICS2018

Markerless tracking of user-defined features with deep learning

Alexander Mathis, Mackenzie Mathis, Matthias Bethge, Pranav Mamidanna

Quantifying behavior is crucial for many applications in neuroscience. Videography provides easy methods for the observation and recording of animal behavior in diverse settings, yet extracting particular aspects of a behavior for further analysis can be h ...

2018

DNN based speaker embedding using content information for text-dependent speaker verification

Petr Motlicek, Subhadeep Dey

In this paper, we are interested in exploring Deep Neural Network (DNN) based speaker embedding for Random-digit task using content information. To this end, a technique is applied to automatically select common phonetic units between the enrollment and te ...

2018

Scalable greedy algorithms for transfer learning

Barbara Caputo, Ilja Kuzborskij

In this paper we consider the binary transfer learning problem, focusing on how to select and combine sources from a large pool to yield a good performance on a target task. Constraining our scenario to real world, we do not assume the direct access to the ...

2017

Synthesizing Robotic Handwriting Motion by Learning from Human Demonstrations

Aude Billard, Hang Yin, Ana Paiva

This paper contributes a novel framework that enables a robotic agent to efficiently learn and synthesize believable handwriting motion. We situate the framework as a foundation with the goal of allowing children to observe, correct and engage with the rob ...

2016

Transfer Learning Through Greedy Subset Selection

Barbara Caputo, Ilja Kuzborskij

We study the binary transfer learning problem, focusing on how to select sources from a large pool and how to combine them to yield a good performance on a target task. In particular, we consider the transfer learning setting where one does not have direct ...

Springer Int Publishing Ag2015

Adaptive regularized diffusion adaptation over multitask networks

Ali H. Sayed, Sadaf Monajemi

The focus of this paper is on multitask learning over adaptive networks where different clusters of nodes have different objectives. We propose an adaptive regularized diffusion strategy using Gaussian kernel regularization to enable the agents to learn ab ...

2015

Domain Adaptation for Microscopy Imaging

Pascal Fua, Carlos Joaquin Becker, Christos Marios Christoudias

Electron and Light Microscopy imaging can now deliver high-quality image stacks of neural structures. However, the amount of human annotation effort required to analyze them remains a major bottleneck. While Machine Learning algorithms can be used to help ...

Institute of Electrical and Electronics Engineers2015

Learning Categories from Few Examples with Multi Model Knowledge Transfer

Barbara Caputo, Tatiana Tommasi

Learning a visual object category from few samples is a compelling and challenging problem. In several real-world applications collecting many annotated data is costly and not always possible. However a small training set does not allow to cover the high i ...

Idiap2013

Transfer Learning by Sharing Support Vectors

Pascal Fua, Carlos Joaquin Becker, Vitaly Henry Ablavsky

Machine Learning techniques play an increasingly vital role in the analysis of Biomedical imagery, as in all other areas of Computer Vision. However, in this specific context, they suffer from the fact that experimental conditions and protocols change ofte ...

2012

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