3,909 research outputs found
Boundary-Seeking Generative Adversarial Networks
Generative adversarial networks (GANs) are a learning framework that rely on
training a discriminator to estimate a measure of difference between a target
and generated distributions. GANs, as normally formulated, rely on the
generated samples being completely differentiable w.r.t. the generative
parameters, and thus do not work for discrete data. We introduce a method for
training GANs with discrete data that uses the estimated difference measure
from the discriminator to compute importance weights for generated samples,
thus providing a policy gradient for training the generator. The importance
weights have a strong connection to the decision boundary of the discriminator,
and we call our method boundary-seeking GANs (BGANs). We demonstrate the
effectiveness of the proposed algorithm with discrete image and character-based
natural language generation. In addition, the boundary-seeking objective
extends to continuous data, which can be used to improve stability of training,
and we demonstrate this on Celeba, Large-scale Scene Understanding (LSUN)
bedrooms, and Imagenet without conditioning
On the Effectiveness of Least Squares Generative Adversarial Networks
Unsupervised learning with generative adversarial networks (GANs) has proven
to be hugely successful. Regular GANs hypothesize the discriminator as a
classifier with the sigmoid cross entropy loss function. However, we found that
this loss function may lead to the vanishing gradients problem during the
learning process. To overcome such a problem, we propose in this paper the
Least Squares Generative Adversarial Networks (LSGANs) which adopt the least
squares loss for both the discriminator and the generator. We show that
minimizing the objective function of LSGAN yields minimizing the Pearson
divergence. We also show that the derived objective function that
yields minimizing the Pearson divergence performs better than the
classical one of using least squares for classification. There are two benefits
of LSGANs over regular GANs. First, LSGANs are able to generate higher quality
images than regular GANs. Second, LSGANs perform more stably during the
learning process. For evaluating the image quality, we conduct both qualitative
and quantitative experiments, and the experimental results show that LSGANs can
generate higher quality images than regular GANs. Furthermore, we evaluate the
stability of LSGANs in two groups. One is to compare between LSGANs and regular
GANs without gradient penalty. We conduct three experiments, including Gaussian
mixture distribution, difficult architectures, and a newly proposed method ---
datasets with small variability, to illustrate the stability of LSGANs. The
other one is to compare between LSGANs with gradient penalty (LSGANs-GP) and
WGANs with gradient penalty (WGANs-GP). The experimental results show that
LSGANs-GP succeed in training for all the difficult architectures used in
WGANs-GP, including 101-layer ResNet
Comparative Study on Generative Adversarial Networks
In recent years, there have been tremendous advancements in the field of
machine learning. These advancements have been made through both academic as
well as industrial research. Lately, a fair amount of research has been
dedicated to the usage of generative models in the field of computer vision and
image classification. These generative models have been popularized through a
new framework called Generative Adversarial Networks. Moreover, many modified
versions of this framework have been proposed in the last two years. We study
the original model proposed by Goodfellow et al. as well as modifications over
the original model and provide a comparative analysis of these models.Comment: 8 pages, 7 figure
Exploring Bias in GAN-based Data Augmentation for Small Samples
For machine learning task, lacking sufficient samples mean the trained model
has low confidence to approach the ground truth function. Until recently, after
the generative adversarial networks (GAN) had been proposed, we see the hope of
small samples data augmentation (DA) with realistic fake data, and many works
validated the viability of GAN-based DA. Although most of the works pointed out
higher accuracy can be achieved using GAN-based DA, some researchers stressed
that the fake data generated from GAN has inherent bias, and in this paper, we
explored when the bias is so low that it cannot hurt the performance, we set
experiments to depict the bias in different GAN-based DA setting, and from the
results, we design a pipeline to inspect specific dataset is
efficiently-augmentable with GAN-based DA or not. And finally, depending on our
trial to reduce the bias, we proposed some advice to mitigate bias in GAN-based
DA application.Comment: rejected by SIGKDD 201
Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving
Generative Adversarial Networks (GAN) have gained a lot of popularity from
their introduction in 2014 till present. Research on GAN is rapidly growing and
there are many variants of the original GAN focusing on various aspects of deep
learning. GAN are perceived as the most impactful direction of machine learning
in the last decade. This paper focuses on the application of GAN in autonomous
driving including topics such as advanced data augmentation, loss function
learning, semi-supervised learning, etc. We formalize and review key
applications of adversarial techniques and discuss challenges and open problems
to be addressed.Comment: Accepted for publication in Electronic Imaging, Autonomous Vehicles
and Machines 2019. arXiv admin note: text overlap with arXiv:1606.05908 by
other author
Language Generation with Recurrent Generative Adversarial Networks without Pre-training
Generative Adversarial Networks (GANs) have shown great promise recently in
image generation. Training GANs for language generation has proven to be more
difficult, because of the non-differentiable nature of generating text with
recurrent neural networks. Consequently, past work has either resorted to
pre-training with maximum-likelihood or used convolutional networks for
generation. In this work, we show that recurrent neural networks can be trained
to generate text with GANs from scratch using curriculum learning, by slowly
teaching the model to generate sequences of increasing and variable length. We
empirically show that our approach vastly improves the quality of generated
sequences compared to a convolutional baseline.Comment: Presented at the 1st Workshop on Learning to Generate Natural
Language at ICML 201
Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classification
In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that
contains a synthetic seed image dataset generation procedure for languages with
different numeral systems using freely available open font file datasets. This
seed dataset of images is then augmented to create a purely synthetic training
dataset, which is in turn used to train a deep neural network and test on
held-out real world handwritten digits dataset spanning five Indic scripts,
Kannada, Tamil, Gujarati, Malayalam, and Devanagari. We showcase the efficacy
of this approach both qualitatively, by training a Boundary-seeking GAN (BGAN)
that generates realistic digit images in the five languages, and also
quantitatively by testing a CNN trained on the synthetic data on the real-world
datasets. This establishes not only an interesting nexus between the
font-datasets-world and transfer learning but also provides a recipe for
universal-digit classification in any script.Comment: Published as a workshop paper at ICLR 2019 (DeepGenStruct-2019
Good Semi-supervised Learning that Requires a Bad GAN
Semi-supervised learning methods based on generative adversarial networks
(GANs) obtained strong empirical results, but it is not clear 1) how the
discriminator benefits from joint training with a generator, and 2) why good
semi-supervised classification performance and a good generator cannot be
obtained at the same time. Theoretically, we show that given the discriminator
objective, good semisupervised learning indeed requires a bad generator, and
propose the definition of a preferred generator. Empirically, we derive a novel
formulation based on our analysis that substantially improves over feature
matching GANs, obtaining state-of-the-art results on multiple benchmark
datasets.Comment: NIPS 2017 camera read
Training Generative Adversarial Networks with Binary Neurons by End-to-end Backpropagation
We propose the BinaryGAN, a novel generative adversarial network (GAN) that
uses binary neurons at the output layer of the generator. We employ the
sigmoid-adjusted straight-through estimators to estimate the gradients for the
binary neurons and train the whole network by end-to-end backpropogation. The
proposed model is able to directly generate binary-valued predictions at test
time. We implement such a model to generate binarized MNIST digits and
experimentally compare the performance for different types of binary neurons,
GAN objectives and network architectures. Although the results are still
preliminary, we show that it is possible to train a GAN that has binary neurons
and that the use of gradient estimators can be a promising direction for
modeling discrete distributions with GANs. For reproducibility, the source code
is available at https://github.com/salu133445/binarygan
PI-REC: Progressive Image Reconstruction Network With Edge and Color Domain
We propose a universal image reconstruction method to represent detailed
images purely from binary sparse edge and flat color domain. Inspired by the
procedures of painting, our framework, based on generative adversarial network,
consists of three phases: Imitation Phase aims at initializing networks,
followed by Generating Phase to reconstruct preliminary images. Moreover,
Refinement Phase is utilized to fine-tune preliminary images into final outputs
with details. This framework allows our model generating abundant high
frequency details from sparse input information. We also explore the defects of
disentangling style latent space implicitly from images, and demonstrate that
explicit color domain in our model performs better on controllability and
interpretability. In our experiments, we achieve outstanding results on
reconstructing realistic images and translating hand drawn drafts into
satisfactory paintings. Besides, within the domain of edge-to-image
translation, our model PI-REC outperforms existing state-of-the-art methods on
evaluations of realism and accuracy, both quantitatively and qualitatively.Comment: 15 pages, 13 figure
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