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Found 27 papers, 14 papers with code
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding
To address this limitation, this study rethinks hyperspectral intrinsic image decomposition for classification tasks by introducing deep feature embedding.
MultiScale Spectral-Spatial Convolutional Transformer for Hyperspectral Image Classification
Due to the powerful ability in capturing the global information, Transformer has become an alternative architecture of CNNs for hyperspectral image classification.
Deep Intrinsic Decomposition with Adversarial Learning for Hyperspectral Image Classification
Convolutional neural networks (CNNs) have been demonstrated their powerful ability to extract discriminative features for hyperspectral image classification.
Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator
Therefore, this work proposes a novel multi-fidelity learning method based on the Fourier Neural Operator by jointing abundant low-fidelity data and limited high-fidelity data under transfer learning paradigm.
Uncertainty Guided Ensemble Self-Training for Semi-Supervised Global Field Reconstruction
Recovering a globally accurate complex physics field from limited sensor is critical to the measurement and control in the aerospace engineering.
RecFNO: a resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operator
The MLP embedding is propitious to more sparse input, while the others benefit from spatial information preservation and perform better with the increase of observation data.
Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network
Then, a self-supervised learning method for training the physics-driven deep multi-fidelity model (PD-DMFM) is proposed, which fully utilizes the physics characteristics of the engineering systems and reduces the dependence on large amounts of labeled low-fidelity data in the training process.
Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders
Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community.
A CNN with Noise Inclined Module and Denoise Framework for Hyperspectral Image Classification
Finally, the CNN with noise inclined module and the denoise framework is developed to obtain discriminative features and provides good classification performance of hyperspectral image.
Transferable Physical Attack against Object Detection with Separable Attention
Transferable adversarial attack is always in the spotlight since deep learning models have been demonstrated to be vulnerable to adversarial samples.
Semi-supervision semantic segmentation with uncertainty-guided self cross supervision
However, the wrong pseudo labeling information generated by cross supervision would confuse the training process and negatively affect the effectiveness of the segmentation model.
Contrastive Enhancement Using Latent Prototype for Few-Shot Segmentation
This paper proposes a contrastive enhancement approach using latent prototypes to leverage latent classes and raise the utilization of similarity information between prototype and query features.
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
However, a lot of labeled data is needed to train CNN, and the common CNN can not quantify the aleatoric uncertainty caused by data noise.
Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis
To solve the above problem, this paper proposes an unsupervised method, i. e., the physics-informed deep Monte Carlo quantile regression method, for reconstructing temperature field and quantifying the aleatoric uncertainty caused by data noise.
A deep learning method based on patchwise training for reconstructing temperature field
To solve the problem, this work proposes a novel deep learning method based on patchwise training to reconstruct the temperature field of electronic equipment accurately from limited observation.
Temperature Field Inversion of Heat-Source Systems via Physics-Informed Neural Networks
In this work, we develop a physics-informed neural network-based temperature field inversion (PINN-TFI) method to solve the TFI-HSS task and a coefficient matrix condition number based position selection of observations (CMCN-PSO) method to select optima positions of noise observations.
Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data
As the construction of data pairs in most engineering problems is time-consuming, data acquisition is becoming the predictive capability bottleneck of most deep surrogate models, which also exists in surrogate for thermal analysis and design.
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack
To bridge the gap between digital attacks and physical attacks, we exploit the full 3D vehicle surface to propose a robust Full-coverage Camouflage Attack (FCA) to fool detectors.
A Machine Learning Surrogate Modeling Benchmark for Temperature Field Reconstruction of Heat-Source Systems
To overcome this problem, this work develops a machine learning modelling benchmark for TFR-HSS task.
RBUE: A ReLU-Based Uncertainty Estimation Method of Deep Neural Networks
Deep Ensemble is widely considered the state-of-the-art method which can estimate the uncertainty with higher quality, but it is very expensive to train and test.
Joint Deep Reversible Regression Model and Physics-Informed Unsupervised Learning for Temperature Field Reconstruction
To solve this problem, this work develops a novel physics-informed deep reversible regression models for temperature field reconstruction of heat-source systems (TFR-HSS), which can better reconstruct the temperature field with limited monitoring points unsupervisedly.
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source Layout
Thermal issue is of great importance during layout design of heat source components in systems engineering, especially for high functional-density products.
Statistical Loss and Analysis for Deep Learning in Hyperspectral Image Classification
To overcome this problem, this work characterizes each class from the hyperspectral image as a statistical distribution and further develops a novel statistical loss with the distributions, not directly with samples for deep learning.
Deep Manifold Embedding for Hyperspectral Image Classification
Deep learning methods have played a more and more important role in hyperspectral image classification.
A novel statistical metric learning for hyperspectral image classification
In this paper, a novel statistical metric learning is developed for spectral-spatial classification of the hyperspectral image.
An End-to-End Joint Unsupervised Learning of Deep Model and Pseudo-Classes for Remote Sensing Scene Representation
Finally, joint learning of the pseudo-center loss and the pseudo softmax loss which is formulated with the samples and the pseudo labels is developed for unsupervised remote sensing scene representation to obtain discriminative representations from the scenes.
Diversity in Machine Learning
Even though the diversity plays an important role in machine learning process, there is no systematical analysis of the diversification in machine learning system.
