287 research outputs found
Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
Generative models that learn disentangled representations for different
factors of variation in an image can be very useful for targeted data
augmentation. By sampling from the disentangled latent subspace of interest, we
can efficiently generate new data necessary for a particular task. Learning
disentangled representations is a challenging problem, especially when certain
factors of variation are difficult to label. In this paper, we introduce a
novel architecture that disentangles the latent space into two complementary
subspaces by using only weak supervision in form of pairwise similarity labels.
Inspired by the recent success of cycle-consistent adversarial architectures,
we use cycle-consistency in a variational auto-encoder framework. Our
non-adversarial approach is in contrast with the recent works that combine
adversarial training with auto-encoders to disentangle representations. We show
compelling results of disentangled latent subspaces on three datasets and
compare with recent works that leverage adversarial training
Neural Face Editing with Intrinsic Image Disentangling
Traditional face editing methods often require a number of sophisticated and
task specific algorithms to be applied one after the other --- a process that
is tedious, fragile, and computationally intensive. In this paper, we propose
an end-to-end generative adversarial network that infers a face-specific
disentangled representation of intrinsic face properties, including shape (i.e.
normals), albedo, and lighting, and an alpha matte. We show that this network
can be trained on "in-the-wild" images by incorporating an in-network
physically-based image formation module and appropriate loss functions. Our
disentangling latent representation allows for semantically relevant edits,
where one aspect of facial appearance can be manipulated while keeping
orthogonal properties fixed, and we demonstrate its use for a number of facial
editing applications.Comment: CVPR 2017 ora
Learning Disentangled Representations with Reference-Based Variational Autoencoders
Learning disentangled representations from visual data, where different
high-level generative factors are independently encoded, is of importance for
many computer vision tasks. Solving this problem, however, typically requires
to explicitly label all the factors of interest in training images. To
alleviate the annotation cost, we introduce a learning setting which we refer
to as "reference-based disentangling". Given a pool of unlabeled images, the
goal is to learn a representation where a set of target factors are
disentangled from others. The only supervision comes from an auxiliary
"reference set" containing images where the factors of interest are constant.
In order to address this problem, we propose reference-based variational
autoencoders, a novel deep generative model designed to exploit the
weak-supervision provided by the reference set. By addressing tasks such as
feature learning, conditional image generation or attribute transfer, we
validate the ability of the proposed model to learn disentangled
representations from this minimal form of supervision
Hyperprior Induced Unsupervised Disentanglement of Latent Representations
We address the problem of unsupervised disentanglement of latent
representations learnt via deep generative models. In contrast to current
approaches that operate on the evidence lower bound (ELBO), we argue that
statistical independence in the latent space of VAEs can be enforced in a
principled hierarchical Bayesian manner. To this effect, we augment the
standard VAE with an inverse-Wishart (IW) prior on the covariance matrix of the
latent code. By tuning the IW parameters, we are able to encourage (or
discourage) independence in the learnt latent dimensions. Extensive
experimental results on a range of datasets (2DShapes, 3DChairs, 3DFaces and
CelebA) show our approach to outperform the -VAE and is competitive with
the state-of-the-art FactorVAE. Our approach achieves significantly better
disentanglement and reconstruction on a new dataset (CorrelatedEllipses) which
introduces correlations between the factors of variation.Comment: AAAI-201
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