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MAMNet: Multi-path Adaptive Modulation Network for Image Super-Resolution
Single image super-resolution (SR) is the process of infer- -
MIMO-OFDM Channel Estimation using 2D and 3D Convolutional Neural Networks
The dataset used in this paper for MIMO-OFDM channel estimation using 2D and 3D Convolutional Neural Networks. -
PowerLinear Activation Functions with application to the first layer of CNNs
Convolutional neural networks (CNNs) have become the state-of-the-art tool for dealing with unsolved problems in computer vision and image processing. -
Dataset for Energy-Efficient Deep Neural Networks
The dataset used in this paper is a collection of 25 state-of-the-art deep neural networks (DNNs) with different architectures and sizes. -
Convolution-based Channel-Frequency Attention for Text-Independent Speaker Ve...
Deep convolutional neural networks (CNNs) have been applied to extracting speaker embeddings with significant success in speaker verification. -
Minimal Filtering Algorithms for Convolutional Neural Networks
The dataset used in this paper is a set of convolutional neural networks with small length FIR filters. -
CIFAR100 and ImageNet
The dataset used in the paper is CIFAR100 and ImageNet. -
Convolutional Sparse Support Estimator Network (CSEN) for Covid-19 Recognitio...
The proposed approach is based on the CSEN that can be seen as a bridge between Deep Learning models and representation-based methods. CSEN uses both a dictionary and a set of... -
Group Equivariant Convolutional Networks
We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting... -
Frequency-Aware Re-Parameterization for Over-Fitting Based Image Compression
Over-fitting based image compression requires weights compactness for compression and fast convergence for practical use, posing challenges for deep convolutional neural... -
Text Classification Dataset
The dataset used for text classification, which is a variant of the typical text classification model based on convolutional operation and max-pooling layer. -
Tied Block Convolution: Leaner and Better CNNs with Shared Thinner Filters
Convolution is the main building block of convolutional neural networks (CNN). We observe that an optimized CNN often has highly correlated filters as the number of channels... -
Quantum CNN Dataset
The dataset used in this paper is a dataset for training quantum convolutional neural networks. -
Quantum CNN
The dataset used in this paper is a quantum convolutional neural network (QCNN) dataset. -
Class Representative Learning Model
The CRL model is based on class-level classifiers, built class-by-class, that would be a representative of instances of a specific class by utilizing activation features of... -
Shuffled Patch-Wise Supervision for Presentation Attack Detection
Face anti-spoofing is essential to prevent false facial verification by using a photo, video, mask, or a different substitute for an authorized person’s face. Most of the... -
Tensor decomposition to Compress Convolutional Layers in Deep Learning
Feature extraction for tensor data serves as an important step in many tasks such as anomaly detection, process monitoring, image classification, and quality control.
