19 research outputs found
Fast Face-swap Using Convolutional Neural Networks
We consider the problem of face swapping in images, where an input identity
is transformed into a target identity while preserving pose, facial expression,
and lighting. To perform this mapping, we use convolutional neural networks
trained to capture the appearance of the target identity from an unstructured
collection of his/her photographs.This approach is enabled by framing the face
swapping problem in terms of style transfer, where the goal is to render an
image in the style of another one. Building on recent advances in this area, we
devise a new loss function that enables the network to produce highly
photorealistic results. By combining neural networks with simple pre- and
post-processing steps, we aim at making face swap work in real-time with no
input from the user
BRUNO: A Deep Recurrent Model for Exchangeable Data
We present a novel model architecture which leverages deep learning tools to
perform exact Bayesian inference on sets of high dimensional, complex
observations. Our model is provably exchangeable, meaning that the joint
distribution over observations is invariant under permutation: this property
lies at the heart of Bayesian inference. The model does not require variational
approximations to train, and new samples can be generated conditional on
previous samples, with cost linear in the size of the conditioning set. The
advantages of our architecture are demonstrated on learning tasks that require
generalisation from short observed sequences while modelling sequence
variability, such as conditional image generation, few-shot learning, and
anomaly detection.Comment: NIPS 201
Folk music style modelling by recurrent neural networks with long short term memory units
We demonstrate two generative models created by training
a recurrent neural network (RNN) with three hidden
layers of long short-term memory (LSTM) units. This extends
past work in numerous directions, including training
deeper models with nearly 24,000 high-level transcriptions
of folk tunes. We discuss our on-going work
Conditional BRUNO : a neural process for exchangeable labelled data
We present a neural process that models exchangeable sequences of high-dimensional complex observations conditionally on a set of labels or tags. Our model combines the expressiveness of deep neural networks with the data-efficiency of Gaussian processes, resulting in a probabilistic model for which the posterior distribution is easy to evaluate and sample from, and the computational complexity scales linearly with the number of observations. The advantages of the proposed architecture are demonstrated on a challenging few-shot view reconstruction task which requires generalisation from short sequences of viewpoints
Discriminative Topic Modeling with Logistic LDA
Despite many years of research into latent Dirichlet allocation (LDA),
applying LDA to collections of non-categorical items is still challenging. Yet
many problems with much richer data share a similar structure and could benefit
from the vast literature on LDA. We propose logistic LDA, a novel
discriminative variant of latent Dirichlet allocation which is easy to apply to
arbitrary inputs. In particular, our model can easily be applied to groups of
images, arbitrary text embeddings, and integrates well with deep neural
networks. Although it is a discriminative model, we show that logistic LDA can
learn from unlabeled data in an unsupervised manner by exploiting the group
structure present in the data. In contrast to other recent topic models
designed to handle arbitrary inputs, our model does not sacrifice the
interpretability and principled motivation of LDA
Efficient Image Gallery Representations at Scale Through Multi-Task Learning
Image galleries provide a rich source of diverse information about a product
which can be leveraged across many recommendation and retrieval applications.
We study the problem of building a universal image gallery encoder through
multi-task learning (MTL) approach and demonstrate that it is indeed a
practical way to achieve generalizability of learned representations to new
downstream tasks. Additionally, we analyze the relative predictive performance
of MTL-trained solutions against optimal and substantially more expensive
solutions, and find signals that MTL can be a useful mechanism to address
sparsity in low-resource binary tasks.Comment: Proceedings of the 43rd International ACM SIGIR Conference on
Research and Development in Information Retrieva
BRUNO : a deep recurrent model for exchangeable data
We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations. Our model is provably exchangeable, meaning that the joint distribution over observations is invariant under permutation: this property lies at the heart of Bayesian inference. The model does not require variational approximations to train, and new samples can be generated conditional on previous samples, with cost linear in the size of the conditioning set. The advantages of our architecture are demonstrated on learning tasks that require generalisation from short observed sequences while modelling sequence variability, such as conditional image generation, few-shot learning, and anomaly detection