60 research outputs found
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
We investigate the non-identifiability issues associated with bidirectional
adversarial training for joint distribution matching. Within a framework of
conditional entropy, we propose both adversarial and non-adversarial approaches
to learn desirable matched joint distributions for unsupervised and supervised
tasks. We unify a broad family of adversarial models as joint distribution
matching problems. Our approach stabilizes learning of unsupervised
bidirectional adversarial learning methods. Further, we introduce an extension
for semi-supervised learning tasks. Theoretical results are validated in
synthetic data and real-world applications.Comment: NIPS 2017 (22 pages); short version (9 pages):
http://people.duke.edu/~cl319/doc/papers/nips_2017_alice.pd
Unsupervised Representation Adversarial Learning Network: from Reconstruction to Generation
A good representation for arbitrarily complicated data should have the
capability of semantic generation, clustering and reconstruction. Previous
research has already achieved impressive performance on either one. This paper
aims at learning a disentangled representation effective for all of them in an
unsupervised way. To achieve all the three tasks together, we learn the forward
and inverse mapping between data and representation on the basis of a symmetric
adversarial process. In theory, we minimize the upper bound of the two
conditional entropy loss between the latent variables and the observations
together to achieve the cycle consistency. The newly proposed RepGAN is tested
on MNIST, fashionMNIST, CelebA, and SVHN datasets to perform unsupervised
classification, generation and reconstruction tasks. The result demonstrates
that RepGAN is able to learn a useful and competitive representation. To the
author's knowledge, our work is the first one to achieve both a high
unsupervised classification accuracy and low reconstruction error on MNIST.
Codes are available at https://github.com/yzhouas/RepGAN-tensorflow
Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
Responses generated by neural conversational models tend to lack
informativeness and diversity. We present Adversarial Information Maximization
(AIM), an adversarial learning strategy that addresses these two related but
distinct problems. To foster response diversity, we leverage adversarial
training that allows distributional matching of synthetic and real responses.
To improve informativeness, our framework explicitly optimizes a variational
lower bound on pairwise mutual information between query and response.
Empirical results from automatic and human evaluations demonstrate that our
methods significantly boost informativeness and diversity.Comment: NIPS 201
Pairwise Augmented GANs with Adversarial Reconstruction Loss
We propose a novel autoencoding model called Pairwise Augmented GANs. We
train a generator and an encoder jointly and in an adversarial manner. The
generator network learns to sample realistic objects. In turn, the encoder
network at the same time is trained to map the true data distribution to the
prior in latent space. To ensure good reconstructions, we introduce an
augmented adversarial reconstruction loss. Here we train a discriminator to
distinguish two types of pairs: an object with its augmentation and the one
with its reconstruction. We show that such adversarial loss compares objects
based on the content rather than on the exact match. We experimentally
demonstrate that our model generates samples and reconstructions of quality
competitive with state-of-the-art on datasets MNIST, CIFAR10, CelebA and
achieves good quantitative results on CIFAR10
Generative Models from the perspective of Continual Learning
Which generative model is the most suitable for Continual Learning? This
paper aims at evaluating and comparing generative models on disjoint sequential
image generation tasks. We investigate how several models learn and forget,
considering various strategies: rehearsal, regularization, generative replay
and fine-tuning. We used two quantitative metrics to estimate the generation
quality and memory ability. We experiment with sequential tasks on three
commonly used benchmarks for Continual Learning (MNIST, Fashion MNIST and
CIFAR10). We found that among all models, the original GAN performs best and
among Continual Learning strategies, generative replay outperforms all other
methods. Even if we found satisfactory combinations on MNIST and Fashion MNIST,
training generative models sequentially on CIFAR10 is particularly instable,
and remains a challenge. Our code is available online
\footnote{\url{https://github.com/TLESORT/Generative\_Continual\_Learning}}
Adversarial Information Factorization
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 object may have many attributes which when altered
do not change the identity of the object itself. Consider the human face; the
identity of a particular person is independent of whether or not they happen to
be wearing glasses. The attribute of wearing glasses can be changed without
changing the identity of the person. However, the ability to manipulate and
alter image attributes without altering the object identity is not a trivial
task. Here, we are interested in learning a representation of the image that
separates the identity of an object (such as a human face) from an attribute
(such as 'wearing glasses'). We demonstrate the success of our factorization
approach by using the learned representation to synthesize the same face with
and without a chosen attribute. We refer to this specific synthesis process as
image attribute manipulation. We further demonstrate that our model achieves
competitive scores, with state of the art, on a facial attribute classification
task
Adversarial Symmetric Variational Autoencoder
A new form of variational autoencoder (VAE) is developed, in which the joint
distribution of data and codes is considered in two (symmetric) forms: ()
from observed data fed through the encoder to yield codes, and () from
latent codes drawn from a simple prior and propagated through the decoder to
manifest data. Lower bounds are learned for marginal log-likelihood fits
observed data and latent codes. When learning with the variational bound, one
seeks to minimize the symmetric Kullback-Leibler divergence of joint density
functions from () and (), while simultaneously seeking to maximize the
two marginal log-likelihoods. To facilitate learning, a new form of adversarial
training is developed. An extensive set of experiments is performed, in which
we demonstrate state-of-the-art data reconstruction and generation on several
image benchmark datasets.Comment: Accepted to NIPS 201
Triangle Generative Adversarial Networks
A Triangle Generative Adversarial Network (-GAN) is developed for
semi-supervised cross-domain joint distribution matching, where the training
data consists of samples from each domain, and supervision of domain
correspondence is provided by only a few paired samples. -GAN consists
of four neural networks, two generators and two discriminators. The generators
are designed to learn the two-way conditional distributions between the two
domains, while the discriminators implicitly define a ternary discriminative
function, which is trained to distinguish real data pairs and two kinds of fake
data pairs. The generators and discriminators are trained together using
adversarial learning. Under mild assumptions, in theory the joint distributions
characterized by the two generators concentrate to the data distribution. In
experiments, three different kinds of domain pairs are considered, image-label,
image-image and image-attribute pairs. Experiments on semi-supervised image
classification, image-to-image translation and attribute-based image generation
demonstrate the superiority of the proposed approach.Comment: To appear in NIPS 201
Adversarially Approximated Autoencoder for Image Generation and Manipulation
Regularized autoencoders learn the latent codes, a structure with the
regularization under the distribution, which enables them the capability to
infer the latent codes given observations and generate new samples given the
codes. However, they are sometimes ambiguous as they tend to produce
reconstructions that are not necessarily faithful reproduction of the inputs.
The main reason is to enforce the learned latent code distribution to match a
prior distribution while the true distribution remains unknown. To improve the
reconstruction quality and learn the latent space a manifold structure, this
work present a novel approach using the adversarially approximated autoencoder
(AAAE) to investigate the latent codes with adversarial approximation. Instead
of regularizing the latent codes by penalizing on the distance between the
distributions of the model and the target, AAAE learns the autoencoder flexibly
and approximates the latent space with a simpler generator. The ratio is
estimated using generative adversarial network (GAN) to enforce the similarity
of the distributions. Additionally, the image space is regularized with an
additional adversarial regularizer. The proposed approach unifies two deep
generative models for both latent space inference and diverse generation. The
learning scheme is realized without regularization on the latent codes, which
also encourages faithful reconstruction. Extensive validation experiments on
four real-world datasets demonstrate the superior performance of AAAE. In
comparison to the state-of-the-art approaches, AAAE generates samples with
better quality and shares the properties of regularized autoencoder with a nice
latent manifold structure
Primal-Dual Wasserstein GAN
We introduce Primal-Dual Wasserstein GAN, a new learning algorithm for
building latent variable models of the data distribution based on the primal
and the dual formulations of the optimal transport (OT) problem. We utilize the
primal formulation to learn a flexible inference mechanism and to create an
optimal approximate coupling between the data distribution and the generative
model. In order to learn the generative model, we use the dual formulation and
train the decoder adversarially through a critic network that is regularized by
the approximate coupling obtained from the primal. Unlike previous methods that
violate various properties of the optimal critic, we regularize the norm and
the direction of the gradients of the critic function. Our model shares many of
the desirable properties of auto-encoding models in terms of mode coverage and
latent structure, while avoiding their undesirable averaging properties, e.g.
their inability to capture sharp visual features when modeling real images. We
compare our algorithm with several other generative modeling techniques that
utilize Wasserstein distances on Frechet Inception Distance (FID) and Inception
Scores (IS).Comment: 14 pages, 16 figure
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