17,757 research outputs found
Triple Generative Adversarial Networks
We propose a unified game-theoretical framework to perform classification and
conditional image generation given limited supervision. It is formulated as a
three-player minimax game consisting of a generator, a classifier and a
discriminator, and therefore is referred to as Triple Generative Adversarial
Network (Triple-GAN). The generator and the classifier characterize the
conditional distributions between images and labels to perform conditional
generation and classification, respectively. The discriminator solely focuses
on identifying fake image-label pairs. Under a nonparametric assumption, we
prove the unique equilibrium of the game is that the distributions
characterized by the generator and the classifier converge to the data
distribution. As a byproduct of the three-player mechanism, Triple-GAN is
flexible to incorporate different semi-supervised classifiers and GAN
architectures. We evaluate Triple-GAN in two challenging settings, namely,
semi-supervised learning and the extreme low data regime. In both settings,
Triple-GAN can achieve excellent classification results and generate meaningful
samples in a specific class simultaneously. In particular, using a commonly
adopted 13-layer CNN classifier, Triple-GAN outperforms extensive
semi-supervised learning methods substantially on more than 10 benchmarks no
matter data augmentation is applied or not
Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks
One of the big restrictions in brain computer interface field is the very
limited training samples, it is difficult to build a reliable and usable system
with such limited data. Inspired by generative adversarial networks, we propose
a conditional Deep Convolutional Generative Adversarial (cDCGAN) Networks
method to generate more artificial EEG signal automatically for data
augmentation to improve the performance of convolutional neural networks in
brain computer interface field and overcome the small training dataset
problems. We evaluate the proposed cDCGAN method on BCI competition dataset of
motor imagery. The results show that the generated artificial EEG data from
Gaussian noise can learn the features from raw EEG data and has no less than
the classification accuracy of raw EEG data in the testing dataset. Also by
using generated artificial data can effectively improve classification accuracy
at the same model with limited training data.Comment: 4 pages, 5 figure
Conditional Infilling GANs for Data Augmentation in Mammogram Classification
Deep learning approaches to breast cancer detection in mammograms have
recently shown promising results. However, such models are constrained by the
limited size of publicly available mammography datasets, in large part due to
privacy concerns and the high cost of generating expert annotations. Limited
dataset size is further exacerbated by substantial class imbalance since
"normal" images dramatically outnumber those with findings. Given the rapid
progress of generative models in synthesizing realistic images, and the known
effectiveness of simple data augmentation techniques (e.g. horizontal
flipping), we ask if it is possible to synthetically augment mammogram datasets
using generative adversarial networks (GANs). We train a class-conditional GAN
to perform contextual in-filling, which we then use to synthesize lesions onto
healthy screening mammograms. First, we show that GANs are capable of
generating high-resolution synthetic mammogram patches. Next, we experimentally
evaluate using the augmented dataset to improve breast cancer classification
performance. We observe that a ResNet-50 classifier trained with GAN-augmented
training data produces a higher AUROC compared to the same model trained only
on traditionally augmented data, demonstrating the potential of our approach.Comment: To appear in MICCAI 2018, Breast Image Analysis Worksho
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
Deep learning methods, and in particular convolutional neural networks
(CNNs), have led to an enormous breakthrough in a wide range of computer vision
tasks, primarily by using large-scale annotated datasets. However, obtaining
such datasets in the medical domain remains a challenge. In this paper, we
present methods for generating synthetic medical images using recently
presented deep learning Generative Adversarial Networks (GANs). Furthermore, we
show that generated medical images can be used for synthetic data augmentation,
and improve the performance of CNN for medical image classification. Our novel
method is demonstrated on a limited dataset of computed tomography (CT) images
of 182 liver lesions (53 cysts, 64 metastases and 65 hemangiomas). We first
exploit GAN architectures for synthesizing high quality liver lesion ROIs. Then
we present a novel scheme for liver lesion classification using CNN. Finally,
we train the CNN using classic data augmentation and our synthetic data
augmentation and compare performance. In addition, we explore the quality of
our synthesized examples using visualization and expert assessment. The
classification performance using only classic data augmentation yielded 78.6%
sensitivity and 88.4% specificity. By adding the synthetic data augmentation
the results increased to 85.7% sensitivity and 92.4% specificity. We believe
that this approach to synthetic data augmentation can generalize to other
medical classification applications and thus support radiologists' efforts to
improve diagnosis.Comment: Preprint submitted to Neurocomputin
DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification
Deep learning has revolutionized the performance of classification, but
meanwhile demands sufficient labeled data for training. Given insufficient
data, while many techniques have been developed to help combat overfitting, the
challenge remains if one tries to train deep networks, especially in the
ill-posed extremely low data regimes: only a small set of labeled data are
available, and nothing -- including unlabeled data -- else. Such regimes arise
from practical situations where not only data labeling but also data collection
itself is expensive. We propose a deep adversarial data augmentation (DADA)
technique to address the problem, in which we elaborately formulate data
augmentation as a problem of training a class-conditional and supervised
generative adversarial network (GAN). Specifically, a new discriminator loss is
proposed to fit the goal of data augmentation, through which both real and
augmented samples are enforced to contribute to and be consistent in finding
the decision boundaries. Tailored training techniques are developed
accordingly. To quantitatively validate its effectiveness, we first perform
extensive simulations to show that DADA substantially outperforms both
traditional data augmentation and a few GAN-based options. We then extend
experiments to three real-world small labeled datasets where existing data
augmentation and/or transfer learning strategies are either less effective or
infeasible. All results endorse the superior capability of DADA in enhancing
the generalization ability of deep networks trained in practical extremely low
data regimes. Source code is available at
https://github.com/SchafferZhang/DADA.Comment: 15 pages, 5 figure
Generative Synthetic Augmentation using Label-to-Image Translation for Nuclei Image Segmentation
In medical image diagnosis, pathology image analysis using semantic
segmentation becomes important for efficient screening as a field of digital
pathology. The spatial augmentation is ordinary used for semantic segmentation.
Tumor images under malignant are rare and to annotate the labels of nuclei
region takes much time-consuming. We require an effective use of dataset to
maximize the segmentation accuracy. It is expected that some augmentation to
transform generalized images influence the segmentation performance. We propose
a synthetic augmentation using label-to-image translation, mapping from a
semantic label with the edge structure to a real image. Exactly this paper deal
with stain slides of nuclei in tumor. Actually, we demonstrate several
segmentation algorithms applied to the initial dataset that contains real
images and labels using synthetic augmentation in order to add their
generalized images. We computes and reports that a proposed synthetic
augmentation procedure improve their accuracy.Comment: 15pages, 12 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
Data Augmentation Generative Adversarial Networks
Effective training of neural networks requires much data. In the low-data
regime, parameters are underdetermined, and learnt networks generalise poorly.
Data Augmentation alleviates this by using existing data more effectively.
However standard data augmentation produces only limited plausible alternative
data. Given there is potential to generate a much broader set of augmentations,
we design and train a generative model to do data augmentation. The model,
based on image conditional Generative Adversarial Networks, takes data from a
source domain and learns to take any data item and generalise it to generate
other within-class data items. As this generative process does not depend on
the classes themselves, it can be applied to novel unseen classes of data. We
show that a Data Augmentation Generative Adversarial Network (DAGAN) augments
standard vanilla classifiers well. We also show a DAGAN can enhance few-shot
learning systems such as Matching Networks. We demonstrate these approaches on
Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and VGG-Face data. In
our experiments we can see over 13% increase in accuracy in the low-data regime
experiments in Omniglot (from 69% to 82%), EMNIST (73.9% to 76%) and VGG-Face
(4.5% to 12%); in Matching Networks for Omniglot we observe an increase of 0.5%
(from 96.9% to 97.4%) and an increase of 1.8% in EMNIST (from 59.5% to 61.3%).Comment: 10 page
Data Augmentation for Deep Candlestick Learner
To successfully build a deep learning model, it will need a large amount of
labeled data. However, labeled data are hard to collect in many use cases. To
tackle this problem, a bunch of data augmentation methods have been introduced
recently and have demonstrated successful results in computer vision, natural
language and so on. For financial trading data, to our best knowledge,
successful data augmentation framework has rarely been studied. Here we propose
a Modified Local Search Attack Sampling method to augment the candlestick data,
which is a very important tool for professional trader. Our results show that
the proposed method can generate high-quality data which are hard to
distinguish by human and will open a new way for finance community to employ
existing machine learning techniques even if the dataset is small.Comment: 12 pages, 9 figures, 2 tables, 1 algorith
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
We present variational generative adversarial networks, a general learning
framework that combines a variational auto-encoder with a generative
adversarial network, for synthesizing images in fine-grained categories, such
as faces of a specific person or objects in a category. Our approach models an
image as a composition of label and latent attributes in a probabilistic model.
By varying the fine-grained category label fed into the resulting generative
model, we can generate images in a specific category with randomly drawn values
on a latent attribute vector. Our approach has two novel aspects. First, we
adopt a cross entropy loss for the discriminative and classifier network, but a
mean discrepancy objective for the generative network. This kind of asymmetric
loss function makes the GAN training more stable. Second, we adopt an encoder
network to learn the relationship between the latent space and the real image
space, and use pairwise feature matching to keep the structure of generated
images. We experiment with natural images of faces, flowers, and birds, and
demonstrate that the proposed models are capable of generating realistic and
diverse samples with fine-grained category labels. We further show that our
models can be applied to other tasks, such as image inpainting,
super-resolution, and data augmentation for training better face recognition
models.Comment: to appear in ICCV 201
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