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SqueezeNet v1.1 and ZynqNet
The dataset used in this paper is the SqueezeNet v1.1 and ZynqNet CNNs, which are characterized by small model size and limited computational requirements. -
Engineering the Neural Collapse Geometry of Supervised-Contrastive Loss
Supervised-contrastive loss (SCL) is an alternative to cross-entropy (CE) for classification tasks that makes use of similarities in the embedding space to allow for richer... -
VoxelHop: Successive Subspace Learning for ALS Disease Classification Using St...
A successive subspace learning model, termed VoxelHop, for accurate classification of Amyotrophic Lateral Sclerosis (ALS) using T2-weighted structural MRI data. -
Road damage detection and classification using deep neural networks with smart...
A dataset for road damage detection and classification using deep neural networks with smartphone images. -
PyBulletGym tasks
The dataset used in the paper is a collection of experiences sampled from a replay buffer, used to train and evaluate the proposed Multi-step DDPG (MDDPG) and Mixed Multi-step... -
fastMRI AXT1 multi-coil brain dataset
The fastMRI AXT1 multi-coil brain dataset is consisted of 248 training volumes (3844 slices) and 82 validation (1424 slices) validation volumes. -
Building Data Genome 2.0 (BDG2.0)
Building Data Genome 2.0 (BDG2.0) dataset comprises hourly time-series data from electrical meters, sourced from the BDG2 project, for modeling purposes. -
Cryo-CARE: Content-Aware Image Restoration for Cryo-Transmission Electron Mic...
Cryo-CARE: a method for content-aware image restoration for cryo-transmission electron microscopy data -
Deep learning and insomnia: assisting clinicians with their diagnosis
Deep learning and insomnia: assisting clinicians with their diagnosis -
Knee Menisci Segmentation and Relaxometry in 3D UTE Cones MR Imaging
The dataset used for knee menisci segmentation and relaxometry in 3D UTE cones MR imaging using attention U-Net with transfer learning -
Imbalanced Gradients
The Imbalanced Gradients dataset is a benchmark for evaluating the robustness of deep neural networks. -
ACCO: Automated Causal CNN Scheduling Optimizer for Real-Time Edge Accelerators
ACCO: Automated Causal CNN Scheduling Optimizer for Real-Time Edge Accelerators -
A Dynamic Domain Adaptation Deep Learning Network for EEG-based Motor Imagery...
This paper presents DADLNet, a dynamic domain adaptation based deep learning network framework for decoding motor imagery tasks. -
ECOVNet: An Ensemble of Deep Convolutional Neural Networks for COVID-19 Detec...
The proposed architecture uses a CNN-based approach to detect COVID-19 from chest X-ray images. The dataset is used to train and evaluate the proposed model. -
COVID CT-Net: Predicting Covid-19 From Chest CT Images Using Attentional Conv...
COVID-19 CT dataset for predicting COVID-19 from chest CT images using attentional convolutional network. -
COVID-19 Severity Classification on Chest X-ray Images
Chest X-ray images dataset used for COVID-19 severity classification -
Atherosclerotic Carotid Plaque Detection using Deep Learning
A small dataset of 65 panoramic images for automatic detection of atherosclerotic carotid plaques. -
Sparse-MLP
Mixture-of-Experts (MoE) architecture, conditional computing, cross-token modeling, Sparse-MLP model -
MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation
Acquiring and annotating sufficient labeled data is crucial in developing accurate and robust learning-based models, but obtaining such data can be challenging in many medical...
