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Thesis

Self-supervised learning using motion and visualizing convolutional neural networks

Abstract:

We propose a novel method for learning convolutional image representations without manual supervision. We use motion in the form of optical-flow, to supervise representations of static images. Training a network to predict flow from a single image can be needlessly difficult due to intrinsic ambiguities in this prediction task. We instead propose two simpler learning goals: (a) embed pixels such that the similarity between their embeddings matches that between their optical-flow vectors (C...

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Department:
University of Oxford

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Department:
University of Oxford
Role:
Examiner
Department:
Universität Bern
Role:
Examiner
Department:
University of Oxford
Role:
Supervisor
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Grant:
677195-IDIU
Funding agency for:
Aravindh Mahendran
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford

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