Search Results for author:
Found 9 papers, 2 papers with code
Low Rank Properties for Estimating Microphones Start Time and Sources Emission Time
Utilizing this augmented low-rank structural information from both LRP and the proposed variants, we formulate four linear constraints on the UTIm.
Are Microphone Signals Alone Sufficient for Self-Positioning?
In an era where asynchronous environments pose challenges to traditional self-positioning methods, we propose a new transformation to the existing paradigm.
Justices for Information Bottleneck Theory
This study comes as a timely response to mounting criticism of the information bottleneck (IB) theory, injecting fresh perspectives to rectify misconceptions and reaffirm its validity.
Attention Mechanism Meets with Hybrid Dense Network for Hyperspectral Image Classification
The resulting \textit{attention-fused hybrid network} (AfNet) is based on three attention-fused parallel hybrid sub-nets with different kernels in each block repeatedly using high-level features to enhance the final ground-truth maps.
Adversarial Stacked Auto-Encoders for Fair Representation Learning
Training machine learning models with the only accuracy as a final goal may promote prejudices and discriminatory behaviors embedded in the data.
On the Fairness of Generative Adversarial Networks (GANs)
Generative adversarial networks (GANs) are one of the greatest advances in AI in recent years.
Hyperspectral Image Classification: Artifacts of Dimension Reduction on Hybrid CNN
Convolutional Neural Networks (CNN) has been extensively studied for Hyperspectral Image Classification (HSIC) more specifically, 2D and 3D CNN models have proved highly efficient in exploiting the spatial and spectral information of Hyperspectral Images.
Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects
Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.
Segmented and Non-Segmented Stacked Denoising Autoencoder for Hyperspectral Band Reduction
Hyperspectral image analysis often requires selecting the most informative bands instead of processing the whole data without losing the key information.
