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CIFAR10 and ImageNet
The dataset used in the paper to evaluate the alignment of deep neural networks with human perception. -
CNN Models
The dataset used in this paper is a large variety of popular CNN models, such as straight-forward, complicated-connected, and grouped architectures. -
PoseAction: Action Recognition for Patients in the Ward using Deep Learning A...
Real-time intelligent detection and prediction of subjects' behavior particularly their movements or actions is critical in the ward. -
Submanifold Sparse Convolutional Networks
Convolutional network are the de-facto standard for analysing spatio-temporal data such as images, videos, 3D shapes, etc. Whilst some of this data is naturally dense (for... -
ResNetX: a more disordered and deeper network architecture
Image classification results on CIFAR-10 and CIFAR-100 benchmarks suggested that our new network architecture performs better than ResNet. -
IntraQ: Learning Synthetic Images with Intra-Class Heterogeneity for Zero-Sho...
Learning to synthesize data has emerged as a promising direction in zero-shot quantization (ZSQ), which represents neural networks by low-bit integer without accessing any of... -
Generative Adversarial Networks
Generative Adversarial Networks (GANs) consist of two networks: a generator G(z) and a discriminator D(x). The discriminator is trying to distinguish real objects from objects... -
MNIST, CIFAR-10, CIFAR-100, Tiny-ImageNet, VGG-like
The dataset used in the paper is MNIST, CIFAR-10, CIFAR-100, Tiny-ImageNet, and VGG-like. -
Sparse Resnet50 model
The dataset used in this paper is a sparse Resnet50 model, which is a variant of the Resnet50 model with 80% sparsity. -
Two-level Group Convolution
The proposed two-level group convolution is suitable for distributed memory computing and robust with respect to the large number of groups. -
ANTNets: Mobile Convolutional Neural Networks for Resource Efficient Image Cla...
Deep convolutional neural networks have achieved remarkable success in computer vision. However, deep neural networks require large computing resources to achieve high... -
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. -
DeiT and ViT models on ImageNet-1k and CIFAR-100
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used DeiT and ViT models on ImageNet-1k and CIFAR-100 datasets. -
DropIT: DROPPING INTERMEDIATE TENSORS FOR MEMORY-EFFICIENT DNN TRAINING
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used DeiT and ViT models on ImageNet-1k and CIFAR-100 datasets. -
Gabor Layers Enhance Network Robustness
The dataset used in this paper is MNIST, SVHN, CIFAR10, CIFAR100, and ImageNet. -
The Theoretical Expressiveness of Maxpooling
The dataset used in this paper is not explicitly described, but it is mentioned that the authors examined the trend away from max pooling in newer architectures. -
Selecting Receptive Fields in Deep Networks
The authors used the CIFAR-10 dataset for evaluating the quality of unsupervised representation learning algorithms. -
DDP: Diffusion Model for Dense Visual Prediction
The DDP framework is a simple, efficient, and powerful framework for dense visual predictions based on conditional diffusion. -
SSIMLayer: Towards Robust Deep Representation Learning via Nonlinear Structur...
The proposed SSIMLayer is a new nonlinear computational layer of high learning capacity to the deep convolutional neural network architectures.
