7,017 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
OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization
Exploring the potential of GANs for unsupervised disentanglement learning,
this paper proposes a novel GAN-based disentanglement framework with One-Hot
Sampling and Orthogonal Regularization (OOGAN). While previous works mostly
attempt to tackle disentanglement learning through VAE and seek to implicitly
minimize the Total Correlation (TC) objective with various sorts of
approximation methods, we show that GANs have a natural advantage in
disentangling with an alternating latent variable (noise) sampling method that
is straightforward and robust. Furthermore, we provide a brand-new perspective
on designing the structure of the generator and discriminator, demonstrating
that a minor structural change and an orthogonal regularization on model
weights entails an improved disentanglement. Instead of experimenting on simple
toy datasets, we conduct experiments on higher-resolution images and show that
OOGAN greatly pushes the boundary of unsupervised disentanglement.Comment: AAAI 202
- …