Generative adversarial networks for data-scarce spectral applications
- URL: http://arxiv.org/abs/2307.07454v1
- Date: Fri, 14 Jul 2023 16:27:24 GMT
- Title: Generative adversarial networks for data-scarce spectral applications
- Authors: Juan Jos\'e Garc\'ia-Esteban, Juan Carlos Cuevas, Jorge Bravo-Abad
- Abstract summary: We report on an application of GANs in the domain of synthetic spectral data generation.
We show that CWGANs can act as a surrogate model with improved performance in the low-data regime.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Generative adversarial networks (GANs) are one of the most robust and
versatile techniques in the field of generative artificial intelligence. In
this work, we report on an application of GANs in the domain of synthetic
spectral data generation, offering a solution to the scarcity of data found in
various scientific contexts. We demonstrate the proposed approach by applying
it to an illustrative problem within the realm of near-field radiative heat
transfer involving a multilayered hyperbolic metamaterial. We find that a
successful generation of spectral data requires two modifications to
conventional GANs: (i) the introduction of Wasserstein GANs (WGANs) to avoid
mode collapse, and, (ii) the conditioning of WGANs to obtain accurate labels
for the generated data. We show that a simple feed-forward neural network
(FFNN), when augmented with data generated by a CWGAN, enhances significantly
its performance under conditions of limited data availability, demonstrating
the intrinsic value of CWGAN data augmentation beyond simply providing larger
datasets. In addition, we show that CWGANs can act as a surrogate model with
improved performance in the low-data regime with respect to simple FFNNs.
Overall, this work highlights the potential of generative machine learning
algorithms in scientific applications beyond image generation and optimization.
Related papers
- cDVGAN: One Flexible Model for Multi-class Gravitational Wave Signal and Glitch Generation [0.7853804618032806]
We present a novel conditional model in the Generative Adrial Network framework for simulating multiple classes of time-domain observations.
Our proposed cDVGAN outperforms 4 different baseline GAN models in replicating the features of the three classes.
Our experiments show that training convolutional neural networks with our cDVGAN-generated data improves the detection of samples embedded in detector noise.
arXiv Detail & Related papers (2024-01-29T17:59:26Z) - LD-GAN: Low-Dimensional Generative Adversarial Network for Spectral
Image Generation with Variance Regularization [72.4394510913927]
Deep learning methods are state-of-the-art for spectral image (SI) computational tasks.
GANs enable diverse augmentation by learning and sampling from the data distribution.
GAN-based SI generation is challenging since the high-dimensionality nature of this kind of data hinders the convergence of the GAN training yielding to suboptimal generation.
We propose a statistical regularization to control the low-dimensional representation variance for the autoencoder training and to achieve high diversity of samples generated with the GAN.
arXiv Detail & Related papers (2023-04-29T00:25:02Z) - Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited
Data [125.7135706352493]
Generative adversarial networks (GANs) typically require ample data for training in order to synthesize high-fidelity images.
Recent studies have shown that training GANs with limited data remains formidable due to discriminator overfitting.
This paper introduces a novel strategy called Adaptive Pseudo Augmentation (APA) to encourage healthy competition between the generator and the discriminator.
arXiv Detail & Related papers (2021-11-12T18:13:45Z) - Improving Model Compatibility of Generative Adversarial Networks by
Boundary Calibration [24.28407308818025]
Boundary-Calibration GANs (BCGANs) are proposed to improve GAN's model compatibility.
BCGANs generate realistic images like original GANs but also achieves superior model compatibility than the original GANs.
arXiv Detail & Related papers (2021-11-03T16:08:09Z) - MineGAN++: Mining Generative Models for Efficient Knowledge Transfer to
Limited Data Domains [77.46963293257912]
We propose a novel knowledge transfer method for generative models based on mining the knowledge that is most beneficial to a specific target domain.
This is done using a miner network that identifies which part of the generative distribution of each pretrained GAN outputs samples closest to the target domain.
We show that the proposed method, called MineGAN, effectively transfers knowledge to domains with few target images, outperforming existing methods.
arXiv Detail & Related papers (2021-04-28T13:10:56Z) - Improving Generative Adversarial Networks with Local Coordinate Coding [150.24880482480455]
Generative adversarial networks (GANs) have shown remarkable success in generating realistic data from some predefined prior distribution.
In practice, semantic information might be represented by some latent distribution learned from data.
We propose an LCCGAN model with local coordinate coding (LCC) to improve the performance of generating data.
arXiv Detail & Related papers (2020-07-28T09:17:50Z) - Partially Conditioned Generative Adversarial Networks [75.08725392017698]
Generative Adversarial Networks (GANs) let one synthesise artificial datasets by implicitly modelling the underlying probability distribution of a real-world training dataset.
With the introduction of Conditional GANs and their variants, these methods were extended to generating samples conditioned on ancillary information available for each sample within the dataset.
In this work, we argue that standard Conditional GANs are not suitable for such a task and propose a new Adversarial Network architecture and training strategy.
arXiv Detail & Related papers (2020-07-06T15:59:28Z) - Generative Adversarial Networks (GANs): An Overview of Theoretical
Model, Evaluation Metrics, and Recent Developments [9.023847175654602]
Generative Adversarial Network (GAN) is an effective method to produce samples of large-scale data distribution.
GANs provide an appropriate way to learn deep representations without widespread use of labeled training data.
In GANs, the generative model is estimated via a competitive process where the generator and discriminator networks are trained simultaneously.
arXiv Detail & Related papers (2020-05-27T05:56:53Z) - Turbulence Enrichment using Physics-informed Generative Adversarial
Networks [0.0]
We develop methods for generative enrichment of turbulence.
We incorporate a physics-informed learning approach by a modification to the loss function.
We show that using the physics-informed learning can also significantly improve the model's ability in generating data that satisfies the physical governing equations.
arXiv Detail & Related papers (2020-03-04T06:14:11Z) - On Leveraging Pretrained GANs for Generation with Limited Data [83.32972353800633]
generative adversarial networks (GANs) can generate highly realistic images, that are often indistinguishable (by humans) from real images.
Most images so generated are not contained in a training dataset, suggesting potential for augmenting training sets with GAN-generated data.
We leverage existing GAN models pretrained on large-scale datasets to introduce additional knowledge, following the concept of transfer learning.
An extensive set of experiments is presented to demonstrate the effectiveness of the proposed techniques on generation with limited data.
arXiv Detail & Related papers (2020-02-26T21:53:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.