3,862 research outputs found
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
Lifelong Generative Modeling
Lifelong learning is the problem of learning multiple consecutive tasks in a
sequential manner, where knowledge gained from previous tasks is retained and
used to aid future learning over the lifetime of the learner. It is essential
towards the development of intelligent machines that can adapt to their
surroundings. In this work we focus on a lifelong learning approach to
unsupervised generative modeling, where we continuously incorporate newly
observed distributions into a learned model. We do so through a student-teacher
Variational Autoencoder architecture which allows us to learn and preserve all
the distributions seen so far, without the need to retain the past data nor the
past models. Through the introduction of a novel cross-model regularizer,
inspired by a Bayesian update rule, the student model leverages the information
learned by the teacher, which acts as a probabilistic knowledge store. The
regularizer reduces the effect of catastrophic interference that appears when
we learn over sequences of distributions. We validate our model's performance
on sequential variants of MNIST, FashionMNIST, PermutedMNIST, SVHN and Celeb-A
and demonstrate that our model mitigates the effects of catastrophic
interference faced by neural networks in sequential learning scenarios.Comment: 32 page
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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