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Found 20 papers, 5 papers with code
A Probabilistic Hadamard U-Net for MRI Bias Field Correction
Random samples drawn from latent space are then incorporated with a prototypical corrected image to generate multiple plausible images.
A novel asymmetrical autoencoder with a sparsifying discrete cosine Stockwell transform layer for gearbox sensor data compression
In comparison to the linear layer, the DCST layer reduces the number of trainable parameters and improves the accuracy of data reconstruction.
Domain Generalization with Fourier Transform and Soft Thresholding
However, it neglects background interference in the amplitude spectrum.
Stein Variational Gradient Descent-based Detection For Random Access With Preambles In MTC
Traditional preamble detection algorithms have low accuracy in the grant-based random access scheme in massive machine-type communication (mMTC).
Electroencephalogram Sensor Data Compression Using An Asymmetrical Sparse Autoencoder With A Discrete Cosine Transform Layer
The latent space data is transmitted to the receiver.
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation
Moreover, our novel array micro-architecture enables adaptive stitching of cells column-wise and row-wise, thereby facilitating perfect parallelism in computations.
Wildfire Detection Via Transfer Learning: A Survey
This paper surveys different publicly available neural network models used for detecting wildfires using regular visible-range cameras which are placed on hilltops or forest lookout towers.
GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classification
To our best knowledge, GazeGNN is the first work that adopts GNN to integrate image and eye-gaze data.
A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer
In this paper, we propose a novel Hadamard Transform (HT)-based neural network layer for hybrid quantum-classical computing.
Multichannel Orthogonal Transform-Based Perceptron Layers for Efficient ResNets
Trainable soft-thresholding layers, that remove noise in the transform domain, bring nonlinearity to the transform domain layers.
Input Normalized Stochastic Gradient Descent Training of Deep Neural Networks
We evaluate the efficiency of our training algorithm on benchmark datasets using ResNet-18, WResNet-20, ResNet-50, and a toy neural network.
Classification of the Cervical Vertebrae Maturation (CVM) stages Using the Tripod Network
They also have a built-in set of novel directional filters that highlight the Cervical Verte edges in X-ray images.
DCT Perceptron Layer: A Transform Domain Approach for Convolution Layer
In this paper, we propose a novel Discrete Cosine Transform (DCT)-based neural network layer which we call DCT-perceptron to replace the $3\times3$ Conv2D layers in the Residual neural Network (ResNet).
Real-time Wireless ECG-derived Respiration Rate Estimation Using an Autoencoder with a DCT Layer
To improve the estimation precision, we propose a neural network that uses a novel Discrete Cosine Transform (DCT) layer to denoise and decorrelates the data.
Multipod Convolutional Network
In this paper, we introduce a convolutional network which we call MultiPodNet consisting of a combination of two or more convolutional networks which process the input image in parallel to achieve the same goal.
Block Walsh-Hadamard Transform Based Binary Layers in Deep Neural Networks
In both 1-D and 2-D layers, we compute the binary WHT of the input feature map and denoise the WHT domain coefficients using a nonlinearity which is obtained by combining soft-thresholding with the tanh function.
Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis
We propose a kernel-PCA based method to detect anomaly in chemical sensors.
Multiplication-Avoiding Variant of Power Iteration with Applications
In this paper, we introduce multiplication-avoiding power iteration (MAPI), which replaces the standard $\ell_2$-inner products that appear at the regular power iteration (RPI) with multiplication-free vector products which are Mercer-type kernel operations related with the $\ell_1$ norm.
Robust Principal Component Analysis Using a Novel Kernel Related with the L1-Norm
We consider a family of vector dot products that can be implemented using sign changes and addition operations only.
Fast Walsh-Hadamard Transform and Smooth-Thresholding Based Binary Layers in Deep Neural Networks
In this paper, we propose a novel layer based on fast Walsh-Hadamard transform (WHT) and smooth-thresholding to replace $1\times 1$ convolution layers in deep neural networks.
