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Learning to Detect Objects with Minimal Supervision

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Pascal Fua, Mathieu Salzmann, Artem Rozantsev

The performance of a classifier trained on data coming from a specific domain typically degrades when applied to a related but different one. While annotating many samples from the new domain would address this issue, it is often too expensive or impractic ...

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Theory and Algorithms for Hypothesis Transfer Learning

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The design and analysis of machine learning algorithms typically considers the problem of learning on a single task, and the nature of learning in such scenario is well explored. On the other hand, very often tasks faced by machine learning systems arrive ...

EPFL2018

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We propose a physically-consistent Bayesian non-parametric approach for fitting Gaussian Mixture Models (GMM) on trajectory data. Physical-consistency of the GMM is ensured by imposing a prior on the component assignments biased by a novel similarity metri ...

2018

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Neuromorphic systems provide brain-inspired methods of computing. In a neuromorphic architecture, inputs are processed by a network of neurons receiving operands through synaptic interconnections, tuned in the process of learning. Neurons act simultaneousl ...

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Object recognition is one of the most important problems in computer vision. However, visual recognition poses many challenges when tried to be reproduced by artificial systems. A main challenge is the problem of variability: objects can appear across huge ...

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Evidence for eligibility traces in human learning

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Whether we prepare a coffee or navigate to a shop: in many tasks we make multiple decisions before reaching a goal. Learning such state-action sequences from sparse reward raises the problem of credit-assignment: which actions out of a long sequence should ...

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Object Detection with Active Sample Harvesting

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The work presented in this dissertation lies in the domains of image classification, object detection, and machine learning. Whether it is training image classifiers or object detectors, the learning phase consists in finding an optimal boundary between po ...

EPFL2017

Object Detection with Active Sample Harvesting

Olivier Canévet

École Polytechnique Fédérale de Lausanne2017