131 datasets found

The proposed End-to-End Deep and Shallow (EEDS) networks method for image super-resolution.

Human pose estimation refers to the task of recognizing postures by localizing body keypoints (head, shoulders, elbows, wrists, knees, ankles, etc.) from images.

Binary neural network (BNN) is an extreme quantization version of convolutional neural networks (CNNs) with all features and weights mapped to just 1-bit.

Deep convolutional neural networks have dominated the pattern recognition scene by providing much more accurate solutions in computer vision problems such as object recognition...

Medical head CT-scan imaging has been successfully combined with deep learning for medical diagnostics of head diseases and lesions. A custom dataset was used for this study.

A convolution neural network (CNN) based classification method for broadband DOA estimation is proposed, where the phase com-ponent of the short-time Fourier transform...

The Speech Recognition dataset is used for speech recognition tasks.

The proposed framework generates a unique set of context-dependent filters based on the input image, and combines them with max-pooling to produce transformation-invariant...

Named entity recognition is a challenging task that has traditionally required large amounts of knowledge in the form of feature engineering and lexicons to achieve high...

Remote sensing image recognition datasets

The APTOS 2019 blindness detection competition dataset is used for training and testing the proposed Frequency Domain Convolutional Neural Network (FDCNN) architecture.

Pathology slides of lung malignancies are classified using resource-frugal convolution neural networks (CNNs) that may be deployed on mobile devices.

This paper proposes a novel knowledge distillation-based learning method to improve the classification performance of convolutional neural networks (CNNs) without a pre-trained...

Deep convolutional neural networks have recently proven extremely competitive in challenging image recognition tasks. This paper proposes the epitomic convolution as a new...

The dataset used in this paper is a CNN convolutional layer loop-nest, which is a 6-level loop-nest representing the convolutional layer of a CNN.

The dataset used in the paper is the VGG dataset, which is a collection of images used for training and testing VGG models.

Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. Specifically, the standard convolution...

The dataset consists of 60,000 images of objects in 200 categories, with 300 images per category.

Convolutional pose machines

MCShips: A large-scale ship dataset for detection and fine-grained categorization in the wild