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FashionMNIST and CIFAR-10
The dataset used in the paper is FashionMNIST and CIFAR-10, which are commonly used datasets for image classification tasks. -
Very Deep Convolutional Networks for Large-Scale Image Recognition
The dataset consists of 60,000 images of objects in 200 categories, with 300 images per category. -
LSUN Bedrooms
The dataset used in the paper is the LSUN bedrooms dataset, a large-scale image dataset. -
Transform Quantization for CNN Compression
The dataset used in this paper is a collection of convolutional neural network (CNN) weights, which are compressed using transform quantization. -
CIFAR10 and ImageNet
The dataset used in the paper to evaluate the alignment of deep neural networks with human perception. -
Churn analysis using deep convolutional neural networks and autoencoders
Customer temporal behavioral data represented as images to perform churn prediction by leveraging deep learning architectures prominent in image classification. -
AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Imag...
The proposed framework for image classification tasks, using a hypersphere representation of deep features. -
MNIST and CIFAR-10 datasets
The MNIST and CIFAR-10 datasets are used to test the theory suggesting the existence of many saddle points in high-dimensional functions. -
COVIDx dataset
The COVIDx dataset is a combination of many publicly available datasets for COVID-19 image classification. -
MobileNetV2
The dataset used in this paper is a MobileNetV2 model, which is a type of deep neural network. The dataset is used to evaluate the performance of the proposed heterogeneous system. -
ResNet50 dataset
The dataset used in this paper is the ResNet50 dataset, a deep neural network trained on the ImageNet dataset. -
Learning Multiple Layers of Features from Tiny Images
The CIFAR-10 dataset consists of 60,000 training images and 10,000 test images. Each image is a 32×32 color image.
