434 datasets found

The dataset used in the paper is a large-scale comparison of pretrained models across computer vision tasks.

Clustering is a fundamental task in data analysis. Recently, deep clustering, which derives inspiration primarily from deep learning approaches, achieves state-of-the-art...

The dataset used for training and testing the proposed method.

The DenseNet dataset is a dataset for image recognition on handwritten digits composed of 60,000 training data and 10,000 test images.

Structural re-parameterization technology provides a new idea to improve the performance of traditional convolutional networks.

The dataset used in this paper is a synthetic dataset of 11940 transmission spectra of exoplanets, generated using the Alfnoor-forward pipeline.

A deep Convolutional Neural Network for topology optimization with strong generalization ability

Deep Matrix Approximately Nonlinear Decomposition (DEMAND) employs Adam and an alternative optimization strategy that is well suited to optimize convex/alternative convex...

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used ResNet-14, ResNet-20, ResNet-56, and ResNet-110 networks, as well as...

A dataset of optical coherence tomography scans showing four different retinal conditions.

The dataset used in this paper is a dataset of four CNN models: ResNet-18, Vgg-16, Squeezenet v1.0, and AlexNet.

The dataset used in this paper is a convolution kernel dataset, which is used to train and evaluate the MetaTune cost model.

Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and...

A collection of tissue-mimicking phantoms for supervised training and evaluation of learned quantitative PAI methods on experimental data.

The COVID-19 Identification ResNet (CIdeR) dataset consists of 517 crowdsourced coughing and breathing audio recordings from 355 participants, of which 62 participants had tested...

High-fidelity image generation with fewer labels

Fast MRI aims to reconstruct a high fidelity image from partially observed measurements. Exuberant development in fast MRI using deep learning has been witnessed recently....

Convolutional LSTM network: A machine learning approach for precipitation nowcasting.

Automated segmentation of tuberculosis (TB)-consistent lesions in chest X-rays (CXRs) using deep learning (DL) methods can help reduce radiologist effort, supplement clinical...

DSAC-C: Constrained Maximum Entropy for Robust Discrete Soft-Actor Critic