4.6k citations · 5.3k across the 20 of their papers we have counts for
12 papers · 1 filter
SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition
Rishabh Kabra, Daniel Zoran, Goker Erdogan +5
To help agents reason about scenes in terms of their building blocks, we wish to extract the compositional structure of any given scene (in particular, the configuration and charac…
Unsupervised Object-Based Transition Models for 3D Partially Observable Environments
Antonia Creswell, Rishabh Kabra, Chris Burgess +1
We present a slot-wise, object-based transition model that decomposes a scene into objects, aligns them (with respect to a slot-wise object memory) to maintain a consistent order a…
AlignNet: Unsupervised Entity Alignment
Antonia Creswell, Kyriacos Nikiforou, Oriol Vinyals +8
Recently developed deep learning models are able to learn to segment scenes into component objects without supervision. This opens many new and exciting avenues of research, allowi…
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…
Adversarial Information Factorization
Antonia Creswell, Yumnah Mohamied, Biswa Sengupta +1
We propose a novel generative model architecture designed to learn representations for images that factor out a single attribute from the rest of the representation. A single objec…
Generative Adversarial Networks: An Overview
Antonia Creswell, Tom White, Vincent Dumoulin +3
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation s…
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…
Denoising Adversarial Autoencoders
Antonia Creswell, Anil Anthony Bharath
Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts of unlabelled data to learn useful representations for inference. Autoencoders, a…
Inverting The Generator Of A Generative Adversarial Network
Antonia Creswell, Anil Anthony Bharath
Generative adversarial networks (GANs) learn to synthesise new samples from a high-dimensional distribution by passing samples drawn from a latent space through a generative networ…
Task Specific Adversarial Cost Function
Antonia Creswell, Anil A. Bharath
The cost function used to train a generative model should fit the purpose of the model. If the model is intended for tasks such as generating perceptually correct samples, it is be…
Adversarial Training For Sketch Retrieval
Antonia Creswell, Anil Anthony Bharath
Generative Adversarial Networks (GAN) are able to learn excellent representations for unlabelled data which can be applied to image generation and scene classification. Representat…