434 datasets found

Latent factor analysis via dynamical systems (LFADS) is an RNN-based variational sequential autoencoder that achieves state-of-the-art performance in denoising high-dimensional...

The dataset used for the experiments on Deep Learning Weather Prediction (DLWP) models, comparing and contrasting backbones on Navier-Stokes and Atmospheric Dynamics.

Image classification results on CIFAR-10 and CIFAR-100 benchmarks suggested that our new network architecture performs better than ResNet.

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...

The dataset used in this paper is ResNet20 and VGG16.

Deep models have advanced prediction in many domains, but their lack of interpretability remains a key barrier to the adoption in many real world applications. This work...

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...

CirCNN is a neural network architecture that uses block-circulant matrices to reduce the number of parameters and computations.

ShuffleNet is an extremely efficient convolutional neural network for mobile devices.

Unitary group convolutions (UGConvs) are a building block for neural networks that combines a group convolution with unitary transforms in feature space.

The proposed framework for image classification tasks, using a hypersphere representation of deep features.

The authors present a photorealistic text-to-image diffusion model with deep language understanding.

Data augmentation has been an indispensable tool to improve the performance of deep neural networks, however the augmentation can hardly transfer among different tasks and...

Rain retrieval algorithm DRAIN using deep learning techniques

The dataset used in the paper is MNIST, CIFAR-10, CIFAR-100, Tiny-ImageNet, and VGG-like.

Lightweight vision transformer for US segmentation.

The dataset used in this paper is a sparse Resnet50 model, which is a variant of the Resnet50 model with 80% sparsity.

The proposed two-level group convolution is suitable for distributed memory computing and robust with respect to the large number of groups.

The dataset used in the paper is the ResNet-50 dataset, a convolutional neural network model.

Deep convolutional neural networks have achieved remarkable success in computer vision. However, deep neural networks require large computing resources to achieve high...