-
Visualizing MuZero Models
MuZero, a model-based reinforcement learning algorithm that uses a value equivalent dynamics model. -
Plug-and-Play Algorithm Convergence Analysis From The Standpoint of Stochasti...
The dataset used in the Plug-and-Play algorithm convergence analysis. -
DAFAR: Defending against Adversaries by Feedback-Autoencoder Reconstruction
Deep learning has shown impressive performance on challenging perceptual tasks and has been widely used in software to provide intelligent services. However, researchers found... -
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. -
Deep neural networks for fast segmentation of 3d medical images
Deep neural networks for fast segmentation of 3d medical images -
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
Gaussian processes (GPs) are non-parametric, flexible models that work well in many tasks. Combining GPs with deep learning methods via deep kernel learning (DKL) is especially... -
Lebanese Road Pothole Detection Dataset
The dataset used for pothole detection using deep learning -
Pothole Detection Dataset
A dataset of images with pothole annotations from various sources, including Google Earth Pro, AUTOPILOT videos, and GoPro camera images. -
DeepGrowth: A Deep Learning Model for Vestibular Schwannoma Growth Prediction
Vestibular schwannoma growth prediction from longitudinal MRI by time-conditioned neural fields -
PID Dataset
A comprehensive dataset for training deep learning algorithms for classifying different types of pavement distress. -
TDT4173 - Method Paper
A survey of the foundations, selected improvements, and some current applications of Deep Convolutional Neural Networks (CNNs). -
Component Training of Turbo Autoencoders
The dataset used in this paper is a serial Turbo Autoencoder (TurboAE) with Gaussian priors (TGP) for component training. -
Hardware-Oriented Acceleration of Deep Convolutional Neural Networks
To address memory and computation resource limitations for hardware-oriented acceleration of deep convolutional neural networks (CNNs), we present a compu-tation flow, stacked... -
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. -
Low Precision Deep Learning Operators
The dataset used in this paper for low precision deep learning operators. -
Phase Extraction Neural Network (PhENN)
The Phase Extraction Neural Network (PhENN) is a deep learning architecture that can be trained to recover an unknown phase object from the raw intensity measurement obtained... -
99mTc-sestamibi SPECT dataset
A dataset of 99mTc-sestamibi SPECT images for automatic reorientation and segmentation of the left ventricle. -
13N-ammonia PET dataset
A dataset of 13N-ammonia PET images for automatic reorientation and segmentation of the left ventricle. -
Multi-Scale Spatial Transformer U-Net for Automatic Reorientation and Segment...
A dataset of 3D nuclear cardiac images for automatic reorientation and segmentation of the left ventricle.
