4.4k citations · 4.5k across the 5 of their papers we have counts for
5 papers
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…
LatentPoison - Adversarial Attacks On The Latent Space
Antonia Creswell, Anil A. Bharath, Biswa Sengupta
Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.) can be subject to attacks using a wide vari…
A Brief Survey of Deep Reinforcement Learning
Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage +1
Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual wor…
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…