This study presents an innovative deep learning-based surrogate model for the Crystal Plasticity Finite Element (CPFE) method, fundamentally transforming the generation of mechanical properties such ...as stress-strain curves in the study of crystal plasticity. Stress-strain curves are pivotal in understanding material deformation, elucidating the intricate relationship between a material's structure and its properties. Traditional CPFE methods, though thorough in their analysis, face significant computational challenges, largely due to the complexity of the crystal plasticity framework. The proposed model circumvents this bottleneck by utilizing an autoencoder architecture to learn intermediate data representations, which are then used to predict the plastic component of deformation. This predicted plastic component serves as a foundation for computing stress-strain curves, effectively bypassing the most time-intensive aspect of traditional CPFE methods, the plasticity self-consistency procedure (achieving a 29.3x speed increase without compromising accuracy).
Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep ...generative adversarial autoencoder (AAE) to identify new molecular fingerprints with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE), which is based on deep neural architectures. In this work, we developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of (a) adjustability in generating molecular fingerprints; (b) capacity of processing very large molecular data sets; and (c) efficiency in unsupervised pretraining for regression model. Our results suggest that the proposed AAE model significantly enhances the capacity and efficiency of development of the new molecules with specific anticancer properties using the deep generative models.
Spectral unmixing is a technique for remotely sensed image interpretation that expresses each (possibly mixed) pixel as a combination of pure spectral signatures (endmembers) and their fractional ...abundances. In this paper, we develop a new technique for unsupervised unmixing which is based on a deep autoencoder network (DAEN). Our newly developed DAEN consists of two parts. The first part of the network adopts stacked autoencoders (SAEs) to learn spectral signatures, so as to generate a good initialization for the unmixing process. In the second part of the network, a variational autoencoder (VAE) is employed to perform blind source separation, aimed at obtaining the endmember signatures and abundance fractions simultaneously. By taking advantage from the SAEs, the robustness of the proposed approach is remarkable as it can unmix data sets with outliers and low signal-to-noise ratio. Moreover, the multihidden layers of the VAE ensure the required constraints (nonnegativity and sum-to-one) when estimating the abundances. The effectiveness of the proposed method is evaluated using both synthetic and real hyperspectral data. When compared with other unmixing methods, the proposed approach demonstrates very competitive performance.
The World Health Organization recognizes pneumonia as a significant global health issue. Artificial intelligence, particularly machine learning, and deep learning has emerged as valuable tools for ...improving pneumonia diagnosis. However, these techniques face a major challenge: the lack of labeled data. To tackle this, we propose using unsupervised learning models, which can produce comparable results even with limited training data. Our study presents an unsupervised learning approach utilizing autoencoders to detect pneumonia from chest X-ray images. Our method uses Variational autoencoders for feature extraction, which are then employed in classification using a Random Forest classifier. The model is trained on a dataset containing two classes of X-ray images: pneumonia and normal. Our approach demonstrates effectiveness comparable to existing supervised learning methods.
Abstract
Variational Autoencoder (VAE), as a kind of deep hidden space generation model, has achieved great success in performance in recent years, especially in image generation. This paper aims to ...study image compression algorithms based on variational autoencoders. This experiment uses the image quality evaluation measurement model, because the image super-resolution algorithm based on interpolation is the most direct and simple method to change the image resolution. In the experiment, the first step of the whole picture is transformed by the variational autoencoder, and then the actual coding is applied to the complete coefficient. Experimental data shows that after encoding using the improved encoding method of the variational autoencoder, the number of bits required for the encoding symbol stream required for transmission or storage in the traditional encoding method is greatly reduced, and symbol redundancy is effectively avoided. The experimental results show that the image research algorithm using variational autoencoder for image 1, image 2, and image 3 reduces the time by 3332, 2637, and 1470 bit respectively compared with the traditional image research algorithm of self-encoding. In the future, people will introduce deep convolutional neural networks to optimize the generative adversarial network, so that the generative adversarial network can obtain better convergence speed and model stability.
Autoencoder is an unsupervised learning model, which can automatically learn data features from a large number of samples and can act as a dimensionality reduction method. With the development of ...deep learning technology, autoencoder has attracted the attention of many scholars. Researchers have proposed several improved versions of autoencoder based on different application fields. First, this paper explains the principle of a conventional autoencoder and investigates the primary development process of an autoencoder. Second, We proposed a taxonomy of autoencoders according to their structures and principles. The related autoencoder models are comprehensively analyzed and discussed. This paper introduces the application progress of autoencoders in different fields, such as image classification and natural language processing, etc. Finally, the shortcomings of the current autoencoder algorithm are summarized, and prospected for its future development directions are addressed.
•The development process of autoencoder.•The application of autoencoder in different fields.•Disadvantages, characteristics and development trend of autoencoder.
Change detection based on heterogeneous images, such as optical images and synthetic aperture radar images, is a challenging problem because of their huge appearance differences. To combat this ...problem, we propose an unsupervised change detection method that contains only a convolutional autoencoder (CAE) for feature extraction and the commonality autoencoder for commonalities exploration. The CAE can eliminate a large part of redundancies in two heterogeneous images and obtain more consistent feature representations. The proposed commonality autoencoder has the ability to discover common features of ground objects between two heterogeneous images by transforming one heterogeneous image representation into another. The unchanged regions with the same ground objects share much more common features than the changed regions. Therefore, the number of common features can indicate changed regions and unchanged regions, and then a difference map can be calculated. At last, the change detection result is generated by applying a segmentation algorithm to the difference map. In our method, the network parameters of the commonality autoencoder are learned by the relevance of unchanged regions instead of the labels. Our experimental results on five real data sets demonstrate the promising performance of the proposed framework compared with several existing approaches.
•A distribution consistency preserving deep embedded clustering model is proposed.•The model exploits GAE and AE to learn node representations and clusters jointly.•A consistency constraint is ...designed to maintain the consistency of the clusters.•The empirical study verifies the effectiveness of the proposed model.
Many complex systems in the real world can be characterized as attributed networks. To mine the potential information in these networks, deep embedded clustering, which obtains node representations and clusters simultaneously, has been given much attention in recent years. Under the assumption of consistency for data in different views, the cluster structure of network topology and that of node attributes should be consistent for an attributed network. However, many existing methods ignore this property, even though they separately encode node representations from network topology and node attributes and cluster nodes on representation vectors learned from one of the views. Therefore, in this study, we propose an end-to-end deep embedded clustering model for attributed networks. It utilizes graph autoencoder and node attribute autoencoder to learn node representations and cluster assignments. In addition, a distribution consistency constraint is introduced to maintain the latent consistency of cluster distributions in two views. Extensive experiments on several datasets demonstrate that the proposed model achieves significantly better or competitive performance compared with the state-of-the-art methods. The source code can be found at https://github.com/Zhengymm/DCP.
Deep learning has gained significant attention in medical image segmentation. However, the limited availability of annotated training data presents a challenge to achieving accurate results. In ...efforts to overcome this challenge, data augmentation techniques have been proposed. However, the majority of these approaches primarily focus on image generation. For segmentation tasks, providing both images and their corresponding target masks is crucial, and the generation of diverse and realistic samples remains a complex task, especially when working with limited training datasets. To this end, we propose a new end-to-end hybrid architecture based on Hamiltonian Variational Autoencoders (HVAE) and a discriminative regularization to improve the quality of generated images. Our method provides an accurate estimation of the joint distribution of the images and masks, resulting in the generation of realistic medical images with reduced artifacts and off-distribution instances. As generating 3D volumes requires substantial time and memory, our architecture operates on a slice-by-slice basis to segment 3D volumes, capitalizing on the richly augmented dataset. Experiments conducted on two public datasets, BRATS (MRI modality) and HECKTOR (PET modality), demonstrate the efficacy of our proposed method on different medical imaging modalities with limited data.
PDF
