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Inverting The Generator Of A Generative Adversarial Network (II)
Antonia Creswell, Anil A Bharath
Generative adversarial networks (GANs) learn a deep generative model that is able to synthesise novel, high-dimensional data samples. New data samples are synthesised by passing la…
Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data
Antonia Creswell, Alison Pouplin, Anil A Bharath
We propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but labelled data is in lim…
On denoising autoencoders trained to minimise binary cross-entropy
Antonia Creswell, Kai Arulkumaran, Anil A. Bharath
Denoising autoencoders (DAEs) are powerful deep learning models used for feature extraction, data generation and network pre-training. DAEs consist of an encoder and decoder which…
Appearance-based indoor localization: A comparison of patch descriptor performance
Jose Rivera-Rubio, Ioannis Alexiou, Anil A. Bharath
Vision is one of the most important of the senses, and humans use it extensively during navigation. We evaluated different types of image and video frame descriptors that could be…