9,903 research outputs found
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
Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach
Recently, semi-supervised learning methods based on generative adversarial
networks (GANs) have received much attention. Among them, two distinct
approaches have achieved competitive results on a variety of benchmark
datasets. Bad GAN learns a classifier with unrealistic samples distributed on
the complement of the support of the input data. Conversely, Triple GAN
consists of a three-player game that tries to leverage good generated samples
to boost classification results. In this paper, we perform a comprehensive
comparison of these two approaches on different benchmark datasets. We
demonstrate their different properties on image generation, and sensitivity to
the amount of labeled data provided. By comprehensively comparing these two
methods, we hope to shed light on the future of GAN-based semi-supervised
learning.Comment: This paper appears at CVPR 2019 Weakly Supervised Learning for
Real-World Computer Vision Applications (LID) Worksho
Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial Learning
We address the problem of segmenting 3D multi-modal medical images in
scenarios where very few labeled examples are available for training.
Leveraging the recent success of adversarial learning for semi-supervised
segmentation, we propose a novel method based on Generative Adversarial
Networks (GANs) to train a segmentation model with both labeled and unlabeled
images. The proposed method prevents over-fitting by learning to discriminate
between true and fake patches obtained by a generator network. Our work extends
current adversarial learning approaches, which focus on 2D single-modality
images, to the more challenging context of 3D volumes of multiple modalities.
The proposed method is evaluated on the problem of segmenting brain MRI from
the iSEG-2017 and MRBrainS 2013 datasets. Significant performance improvement
is reported, compared to state-of-art segmentation networks trained in a
fully-supervised manner. In addition, our work presents a comprehensive
analysis of different GAN architectures for semi-supervised segmentation,
showing recent techniques like feature matching to yield a higher performance
than conventional adversarial training approaches. Our code is publicly
available at https://github.com/arnab39/FewShot_GAN-Unet3DComment: submitted to Medical Image Analysis for revie
Manifold regularization with GANs for semi-supervised learning
Generative Adversarial Networks are powerful generative models that are able
to model the manifold of natural images. We leverage this property to perform
manifold regularization by approximating a variant of the Laplacian norm using
a Monte Carlo approximation that is easily computed with the GAN. When
incorporated into the semi-supervised feature-matching GAN we achieve
state-of-the-art results for GAN-based semi-supervised learning on CIFAR-10 and
SVHN benchmarks, with a method that is significantly easier to implement than
competing methods. We also find that manifold regularization improves the
quality of generated images, and is affected by the quality of the GAN used to
approximate the regularizer
Semi-Supervised Learning with IPM-based GANs: an Empirical Study
We present an empirical investigation of a recent class of Generative
Adversarial Networks (GANs) using Integral Probability Metrics (IPM) and their
performance for semi-supervised learning. IPM-based GANs like Wasserstein GAN,
Fisher GAN and Sobolev GAN have desirable properties in terms of theoretical
understanding, training stability, and a meaningful loss. In this work we
investigate how the design of the critic (or discriminator) influences the
performance in semi-supervised learning. We distill three key take-aways which
are important for good SSL performance: (1) the K+1 formulation, (2) avoiding
batch normalization in the critic and (3) avoiding gradient penalty constraints
on the classification layer.Comment: Appeared at NIPS 2017 Workshop: Deep Learning: Bridging Theory and
Practic
Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification
In this paper, we address the hyperspectral image (HSI) classification task
with a generative adversarial network and conditional random field (GAN-CRF)
-based framework, which integrates a semi-supervised deep learning and a
probabilistic graphical model, and make three contributions. First, we design
four types of convolutional and transposed convolutional layers that consider
the characteristics of HSIs to help with extracting discriminative features
from limited numbers of labeled HSI samples. Second, we construct
semi-supervised GANs to alleviate the shortage of training samples by adding
labels to them and implicitly reconstructing real HSI data distribution through
adversarial training. Third, we build dense conditional random fields (CRFs) on
top of the random variables that are initialized to the softmax predictions of
the trained GANs and are conditioned on HSIs to refine classification maps.
This semi-supervised framework leverages the merits of discriminative and
generative models through a game-theoretical approach. Moreover, even though we
used very small numbers of labeled training HSI samples from the two most
challenging and extensively studied datasets, the experimental results
demonstrated that spectral-spatial GAN-CRF (SS-GAN-CRF) models achieved
top-ranking accuracy for semi-supervised HSI classification.Comment: Accepted by IEEE T-CY
Semi-supervised Rare Disease Detection Using Generative Adversarial Network
Rare diseases affect a relatively small number of people, which limits
investment in research for treatments and cures. Developing an efficient method
for rare disease detection is a crucial first step towards subsequent clinical
research. In this paper, we present a semi-supervised learning framework for
rare disease detection using generative adversarial networks. Our method takes
advantage of the large amount of unlabeled data for disease detection and
achieves the best results in terms of precision-recall score compared to
baseline techniques.Comment: Machine Learning for Health (ML4H) Workshop at NeurIPS 2018
arXiv:1811.0721
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
Unsupervised and semi-supervised learning with Categorical Generative Adversarial Networks assisted by Wasserstein distance for dermoscopy image Classification
Melanoma is a curable aggressive skin cancer if detected early. Typically,
the diagnosis involves initial screening with subsequent biopsy and
histopathological examination if necessary. Computer aided diagnosis offers an
objective score that is independent of clinical experience and the potential to
lower the workload of a dermatologist. In the recent past, success of deep
learning algorithms in the field of general computer vision has motivated
successful application of supervised deep learning methods in computer aided
melanoma recognition. However, large quantities of labeled images are required
to make further improvements on the supervised method. A good annotation
generally requires clinical and histological confirmation, which requires
significant effort. In an attempt to alleviate this constraint, we propose to
use categorical generative adversarial network to automatically learn the
feature representation of dermoscopy images in an unsupervised and
semi-supervised manner. Thorough experiments on ISIC 2016 skin lesion chal-
lenge demonstrate that the proposed feature learning method has achieved an
average precision score of 0.424 with only 140 labeled images. Moreover, the
proposed method is also capable of generating real-world like dermoscopy
images
Sobolev GAN
We propose a new Integral Probability Metric (IPM) between distributions: the
Sobolev IPM. The Sobolev IPM compares the mean discrepancy of two distributions
for functions (critic) restricted to a Sobolev ball defined with respect to a
dominant measure . We show that the Sobolev IPM compares two distributions
in high dimensions based on weighted conditional Cumulative Distribution
Functions (CDF) of each coordinate on a leave one out basis. The Dominant
measure plays a crucial role as it defines the support on which
conditional CDFs are compared. Sobolev IPM can be seen as an extension of the
one dimensional Von-Mises Cram\'er statistics to high dimensional
distributions. We show how Sobolev IPM can be used to train Generative
Adversarial Networks (GANs). We then exploit the intrinsic conditioning implied
by Sobolev IPM in text generation. Finally we show that a variant of Sobolev
GAN achieves competitive results in semi-supervised learning on CIFAR-10,
thanks to the smoothness enforced on the critic by Sobolev GAN which relates to
Laplacian regularization
- …