Hyperspectral Target Detection with an Auxiliary Generative Adversarial Network

In recent years, the deep neural network has shown a strong presence in classification tasks and its effectiveness has been well proved. However, the framework of DNN usually requires a large number of samples. Compared to the training sets in classification tasks, the training sets for the target detection of hyperspectral images may only include a few target spectra which are quite limited and precious. The insufficient labeled samples make the DNN-based hyperspectral target detection task a challenging problem. To address this problem, we propose a hyperspectral target detection approach with an auxiliary generative adversarial network. Specifically, the training set is first expanded by generating simulated target spectra and background spectra using the generative adversarial network. Then, a classifier which is highly associated with the discriminator of the generative adversarial network is trained based on the real and the generated spectra. Finally, in order to further suppress the background, guided filters are utilized to improve the smoothness and robustness of the detection results. Experiments conducted on real hyperspectral images show the proposed approach is able to perform more efficiently and accurately compared to other target detection approaches.

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EdgeGAN: One-way mapping generative adversarial network based on the edge information for unpaired training set

Journal of Visual Communication and Image Representation ◽

10.1016/j.jvcir.2021.103187 ◽

2021 ◽

pp. 103187

Author(s):

Yijie Li ◽

Qiaokang Liang ◽

Zhengwei Li ◽

Youcheng Lei ◽

Wei Sun ◽

...

Keyword(s):

Training Set ◽

Generative Adversarial Network ◽

Edge Information ◽

Adversarial Network

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Unsupervised Feature Extraction in Hyperspectral Images Based on Wasserstein Generative Adversarial Network

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2018.2876123 ◽

2019 ◽

Vol 57 (5) ◽

pp. 2669-2688 ◽

Cited By ~ 27

Author(s):

Mingyang Zhang ◽

Maoguo Gong ◽

Yishun Mao ◽

Jun Li ◽

Yue Wu

Keyword(s):

Feature Extraction ◽

Hyperspectral Images ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Unsupervised Feature Extraction

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1D conditional generative adversarial network for spectrum-to-spectrum translation of simulated chemical reflectance signatures

Journal of Spectral Imaging ◽

10.1255/jsi.2021.a2 ◽

2021 ◽

Author(s):

Cara Murphy ◽

John Kerekes

Keyword(s):

Classification Accuracy ◽

Domain Adaptation ◽

Real Data ◽

Training Set ◽

Generative Adversarial Network ◽

Average Classification Accuracy ◽

Adversarial Network ◽

Chemical Residues ◽

Reflectance Data

The classification of trace chemical residues through active spectroscopic sensing is challenging due to the lack of physics-based models that can accurately predict spectra. To overcome this challenge, we leveraged the field of domain adaptation to translate data from the simulated to the measured domain for training a classifier. We developed the first 1D conditional generative adversarial network (GAN) to perform spectrum-to-spectrum translation of reflectance signatures. We applied the 1D conditional GAN to a library of simulated spectra and quantified the improvement in classification accuracy on real data using the translated spectra for training the classifier. Using the GAN-translated library, the average classification accuracy increased from 0.622 to 0.723 on real chemical reflectance data, including data from chemicals not included in the GAN training set.

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A De Novo Molecular Generation Method Using Latent Vector Based Generative Adversarial Network

10.26434/chemrxiv.8299544.v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Oleksii Prykhodko ◽

Simon Viet Johansson ◽

Panagiotis-Christos Kotsias ◽

Esben Jannik Bjerrum ◽

Ola Engkvist ◽

...

Keyword(s):

Deep Learning ◽

De Novo ◽

Chemical Space ◽

Learning Method ◽

Training Set ◽

Generative Adversarial Network ◽

Structure Generation ◽

Adversarial Network ◽

Molecule Design ◽

Novel Structures

Recently deep learning method has been used for generating novel structures. In the current study, we proposed a new deep learning method, LatentGAN, which combine an autoencoder and a generative adversarial neural network for doing de novo molecule design. We applied the method for structure generation in two scenarios, one is to generate random drug-like compounds and the other is to generate target biased compounds. Our results show that the method works well in both cases, in which sampled compounds from the trained model can largely occupy the same chemical space of the training set and still a substantial fraction of the generated compound are novel. The distribution of drug-likeness score for compounds sampled from LatentGAN is also similar to that of the training set.

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A De Novo Molecular Generation Method Using Latent Vector Based Generative Adversarial Network

10.26434/chemrxiv.8299544.v3 ◽

2019 ◽

Cited By ~ 1

Author(s):

Oleksii Prykhodko ◽

Simon Viet Johansson ◽

Panagiotis-Christos Kotsias ◽

Josep Arús-Pous ◽

Esben Jannik Bjerrum ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

De Novo ◽

Molecular Design ◽

Chemical Space ◽

Training Set ◽

Generative Adversarial Network ◽

Adversarial Network ◽

De Novo Molecular Design ◽

Novel Structures

Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generative adversarial neural network for de novo molecular design. We applied the method in two scenarios: one to generate random drug-like compounds and another to generate target-biased compounds. Our results show that the method works well in both cases: sampled compounds from the trained model can largely occupy the same chemical space as the training set and also generate a substantial fraction of novel compounds. Moreover, the drug-likeness score of compounds sampled from LatentGAN is also similar to that of the training set. Lastly, generated compounds differ from those obtained with a Recurrent Neural Network-based generative model approach, indicating that both methods can be used complementarily.

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Data Augmentation based on Sequence Generative Adversarial Network for Chinese Clinical Named Entity Recognition (Preprint)

10.2196/preprints.17847 ◽

2020 ◽

Author(s):

蓬辉王

Keyword(s):

Data Augmentation ◽

Named Entity Recognition ◽

Basic Element ◽

Entity Recognition ◽

Training Set ◽

Generative Adversarial Network ◽

Named Entity ◽

Adversarial Network ◽

External Resource ◽

Medical Domain

BACKGROUND Chinese clinical named entity recognition, as a fundamental task of Chinese medical information extraction, plays an important role in recognizing medical entities contained in Chinese electronic medical records. Limited to lack of large annotated data, existing methods concentrate on employing external resources to improve the performance of clinical named entity recognition, which require lots of time and efficient rules to add external resources. OBJECTIVE To solve the problem of lack of large annotated data, we employ data augmentation without external resource to automatically generate more medical data depending on entities and non-entities in the training set, and enlarge training dataset to improve the performance of named entity recognition. METHODS In this paper, we propose a method of data augmentation, based on sequence generative adversarial network, to enlarge the training set. Different from other sequence generative adversarial networks, where the basic element is character or word, the basic element of our generated sequence is entity or non-entity. In our model, the generator can generate new sentences composed of entities and non-entities based on the learned hidden relationship between the entities and non-entities in the training set and the discriminator can judge if the generated sentences are positive and give rewards to help train the generator. The generated data from sequence adversarial network is used to enlarge the training set and improve the performance of named entity recognition in medical records. RESULTS Without external resource, we employ our data augmentation method in three datasets, both in general domains and medical domain. Experiments show that when we use generated data from data augmentation to expand training set, named entity recognition system has achieved competitive performance compared with existing methods, which shows the effectiveness of our data augmentation method. In general domains, our method achieves an overall F1-score of 59.42% in Weibo NER dataset and a F1-score of 95.28% in Resume. In medical domain, our method achieves 83.40%. CONCLUSIONS Our data augmentation method can expand training set based on the hidden relationship between entities and non-entities in the dataset, which can alleviate the problem of lack of labeled data while avoid using external resource. At the same time, our method can improve the performance of named entity recognition not only in general domains but also medical domain.

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A de novo molecular generation method using latent vector based generative adversarial network

Journal of Cheminformatics ◽

10.1186/s13321-019-0397-9 ◽

2019 ◽

Vol 11 (1) ◽

Cited By ~ 24

Author(s):

Oleksii Prykhodko ◽

Simon Viet Johansson ◽

Panagiotis-Christos Kotsias ◽

Josep Arús-Pous ◽

Esben Jannik Bjerrum ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

De Novo ◽

Molecular Design ◽

Chemical Space ◽

Training Set ◽

Generative Adversarial Network ◽

Adversarial Network ◽

De Novo Molecular Design ◽

Novel Structures

AbstractDeep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generative adversarial neural network for de novo molecular design. We applied the method in two scenarios: one to generate random drug-like compounds and another to generate target-biased compounds. Our results show that the method works well in both cases. Sampled compounds from the trained model can largely occupy the same chemical space as the training set and also generate a substantial fraction of novel compounds. Moreover, the drug-likeness score of compounds sampled from LatentGAN is also similar to that of the training set. Lastly, generated compounds differ from those obtained with a Recurrent Neural Network-based generative model approach, indicating that both methods can be used complementarily.

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CR-GAN: Learning Complete Representations for Multi-view Generation

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/131 ◽

2018 ◽

Cited By ~ 34

Author(s):

Yu Tian ◽

Xi Peng ◽

Long Zhao ◽

Shaoting Zhang ◽

Dimitris N. Metaxas

Keyword(s):

Supervised Learning ◽

State Of The Art ◽

Unlabeled Data ◽

Challenging Problem ◽

Generative Adversarial Network ◽

Single View ◽

Adversarial Network ◽

Complete Representations ◽

Wide Range ◽

And Robotics

Generating multi-view images from a single-view input is an important yet challenging problem. It has broad applications in vision, graphics, and robotics. Our study indicates that the widely-used generative adversarial network (GAN) may learn ?incomplete? representations due to the single-pathway framework: an encoder-decoder network followed by a discriminator network.We propose CR-GAN to address this problem. In addition to the single reconstruction path, we introduce a generation sideway to maintain the completeness of the learned embedding space. The two learning paths collaborate and compete in a parameter-sharing manner, yielding largely improved generality to ?unseen? dataset. More importantly, the two-pathway framework makes it possible to combine both labeled and unlabeled data for self-supervised learning, which further enriches the embedding space for realistic generations. We evaluate our approach on a wide range of datasets. The results prove that CR-GAN significantly outperforms state-of-the-art methods, especially when generating from ?unseen? inputs in wild conditions.

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Improved Active Deep Learning for Semi-Supervised Classification of Hyperspectral Image

Remote Sensing ◽

10.3390/rs14010171 ◽

2021 ◽

Vol 14 (1) ◽

pp. 171

Author(s):

Qingyan Wang ◽

Meng Chen ◽

Junping Zhang ◽

Shouqiang Kang ◽

Yujing Wang

Keyword(s):

Deep Learning ◽

Supervised Classification ◽

Hyperspectral Image ◽

Graph Algorithm ◽

Hyperspectral Images ◽

Training Set ◽

Proposed Model ◽

Deep Networks ◽

Classification Tasks

Hyperspectral image (HSI) data classification often faces the problem of the scarcity of labeled samples, which is considered to be one of the major challenges in the field of remote sensing. Although active deep networks have been successfully applied in semi-supervised classification tasks to address this problem, their performance inevitably meets the bottleneck due to the limitation of labeling cost. To address the aforementioned issue, this paper proposes a semi-supervised classification method for hyperspectral images that improves active deep learning. Specifically, the proposed model introduces the random multi-graph algorithm and replaces the expert mark in active learning with the anchor graph algorithm, which can label a considerable amount of unlabeled data precisely and automatically. In this way, a large number of pseudo-labeling samples would be added to the training subsets such that the model could be fine-tuned and the generalization performance could be improved without extra efforts for data manual labeling. Experiments based on three standard HSIs demonstrate that the proposed model can get better performance than other conventional methods, and they also outperform other studied algorithms in the case of a small training set.

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