DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN

Lim, Swee Kiat; Loo, Yi; Tran, Ngoc-Trung; Cheung, Ngai-Man; Roig, Gemma; Elovici, Yuval

doi:10.1109/icdm.2018.00146

Cited by 96 publications

(40 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is an important fundamental problem with active AI research and several promising AI directions: First, more sophisticated data augmentation techniques have recently been proposed to enrich the training datasets to better characterize data distribution. These techniques include feature space data augmentation [42,43] and data synthesis using complex deep neural networks models, notably generative adversarial networks (GAN) [44,45,46,47]. Second, there are substantial interests to improve semisupervised learning: learning methods that typically use a small amount of labeled data and unlabeled data [48].…”

Section: Discussionmentioning

confidence: 99%

Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study

Tran

et al. 2019

JCM

Self Cite

310

213

View full text Add to dashboard Cite

The increasing application of Artificial Intelligence (AI) in health and medicine has attracted a great deal of research interest in recent decades. This study aims to provide a global and historical picture of research concerning AI in health and medicine. A total of 27,451 papers that were published between 1977 and 2018 (84.6% were dated 2008–2018) were retrieved from the Web of Science platform. The descriptive analysis examined the publication volume, and authors and countries collaboration. A global network of authors’ keywords and content analysis of related scientific literature highlighted major techniques, including Robotic, Machine learning, Artificial neural network, Artificial intelligence, Natural language process, and their most frequent applications in Clinical Prediction and Treatment. The number of cancer-related publications was the highest, followed by Heart Diseases and Stroke, Vision impairment, Alzheimer’s, and Depression. Moreover, the shortage in the research of AI application to some high burden diseases suggests future directions in AI research. This study offers a first and comprehensive picture of the global efforts directed towards this increasingly important and prolific field of research and suggests the development of global and national protocols and regulations on the justification and adaptation of medical AI products.

show abstract

Section: Discussionmentioning

confidence: 99%

Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study

Tran

et al. 2019

JCM

Self Cite

310

213

View full text Add to dashboard Cite

show abstract

“…The combination of our proposed techniques leads to state of the art FID scores on benchmark datasets. Future work applies our model for other applications, such as: person re-identification (Guo and Cheung 2012), anomaly detection (Lim et al 2018).…”

Section: Resultsmentioning

confidence: 99%

Improving GAN with Neighbors Embedding and Gradient Matching

Tran

Bui

Cheung

2019

AAAI

Self Cite

View full text Add to dashboard Cite

We propose two new techniques for training Generative Adversarial Networks (GANs). Our objectives are to alleviate mode collapse in GAN and improve the quality of the generated samples. First, we propose neighbor embedding, a manifold learning-based regularization to explicitly retain local structures of latent samples in the generated samples. This prevents generator from producing nearly identical data samples from different latent samples, and reduces mode collapse. We propose an inverse t-SNE regularizer to achieve this. Second, we propose a new technique, gradient matching, to align the distributions of the generated samples and the real samples. As it is challenging to work with high-dimensional sample distributions, we propose to align these distributions through the scalar discriminator scores. We constrain the difference between the discriminator scores of the real samples and generated ones. We further constrain the difference between the gradients of these discriminator scores. We derive these constraints from Taylor approximations of the discriminator function. We perform experiments to demonstrate that our proposed techniques are computationally simple and easy to be incorporated in existing systems. When Gradient matching and Neighbour embedding are applied together, our GN-GAN achieves outstanding results on 1D/2D synthetic, CIFAR-10 and STL-10 datasets, e.g. FID score of 30.80 for the STL-10 dataset. Our code is available at: https://github.com/tntrung/gan * These authors have contributed equally to this work

show abstract

“…In contrast to these methods, our proposed GANDA uses autoencoders and GANs to generate minority class data. Please note that DOPING [27] originally aimed to detect anomaly events, but it can be used for augmenting minority class data, because oversampling anomaly samples at the boundary of the latent distributions is equivalent to augmenting minority class data. While DOPING also adopted autoencoders and GANs, the latent space is not tailored by class conditioning for more accurate oversampling, which is not similar to our proposed method.…”

Section: Methods For Imbalanced Datasetsmentioning

confidence: 99%

Generative Adversarial Network for Class-Conditional Data Augmentation

2020

View full text Add to dashboard Cite

We propose a novel generative adversarial network for class-conditional data augmentation (i.e., GANDA) to mitigate data imbalance problems in image classification tasks. The proposed GANDA generates minority class data by exploiting majority class information to enhance the classification accuracy of minority classes. For stable GAN training, we introduce a new denoising autoencoder initialization with explicit class conditioning in the latent space, which enables the generation of definite samples. The generated samples are visually realistic and have a high resolution. Experimental results demonstrate that the proposed GANDA can considerably improve classification accuracy, especially when datasets are highly imbalanced on standard benchmark datasets (i.e., MNIST and CelebA). Our generated samples can be easily used to train conventional classifiers to enhance their classification accuracy.

show abstract

DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN

Cited by 96 publications

References 11 publications

Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study

Global Evolution of Research in Artificial Intelligence in Health and Medicine: A Bibliometric Study

Improving GAN with Neighbors Embedding and Gradient Matching

Generative Adversarial Network for Class-Conditional Data Augmentation

Contact Info

Product

Resources

About