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Self-supervised Face Representation Learning

Sharma, Vivek


This thesis investigates fine-tuning deep face features in a self-supervised manner for discriminative face representation learning, wherein we develop methods to automatically generate pseudo-labels for training a neural network. Most importantly solving this problem helps us to advance the state-of-the-art in representation learning and can be beneficial to a variety of practical downstream tasks. Fortunately, there is a vast amount of videos on the internet that can be used by machines to learn an effective representation. We present methods that can learn a strong face representation from large-scale data be the form of images or video.

However, while learning a good representation using a deep learning algorithm requires a large-scale dataset with manually curated labels, we propose self-supervised approaches to generate pseudo-labels utilizing the temporal structure of the video data and similarity constraints to get supervision from the data itself.

We aim to learn a representation that exhibits small distances between samples from the same person, and large inter-person distances in feature space. Using metric learning one could achieve that as it is comprised of a pull-term, pulling data points from the same class closer, and a push-term, pushing data points from a different class further away. ... mehr

Volltext §
DOI: 10.5445/IR/1000119819
Veröffentlicht am 02.06.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Hochschulschrift
Publikationsdatum 02.06.2020
Sprache Englisch
Identifikator KITopen-ID: 1000119819
Verlag Karlsruher Institut für Technologie (KIT)
Umfang XXIII, 149 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Informatik (INFORMATIK)
Institut Institut für Anthropomatik und Robotik (IAR)
Prüfungsdatum 14.05.2020
Schlagwörter Video Understanding, Video Face Clustering, Self-Supervised Learning, Representation Learning, Siamese Networks, Variational Autoencoders, Feature Encoding
Referent/Betreuer Stiefelhagen, R.
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