CNN’s Jake Tapper Breaks News of Paramount Buying Network’s Parent Company WBD Live on Air…
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Hguimaraes/gtzan.keras
[REPO] Music Genre classification on GTZAN dataset using CNNs (⭐ 209)
Using CNNs For Users Segmentation In Video See-Through Augmented Virtuality
In this paper, we present preliminary results on the use of deep learning techniques to integrate the users self-body and other participants into a head-mounted video see-through augmented virtuality scenario. It has been previously shown that seeing...
Non-Local Video Denoising by CNN
Non-local patch based methods were until recently state-of-the-art for image denoising but are now outperformed by CNNs. Yet they are still the state-of-the-art for video denoising, as video redundancy is a key factor to attain high denoising perform...
CNN’s Dana Bash ‘doesn’t know what side of the aisle’ neo-Nazi marchers in Ohio come from
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Snowden Debates CNN’s Fareed Zakaria on Encryption
Points: 19 | Comments: 2 | Author: etiam
Distinguishing artefacts: evaluating the saturation point of convolutional neural networks
Prior work has shown Convolutional Neural Networks (CNNs) trained on surrogate Computer Aided Design (CAD) models are able to detect and classify real-world artefacts from photographs. The applications of which support twinning of digital and physica...
Context-aware Neural-based Dialog Act Classification on Automatically Generated Transcript…
This paper presents our latest investigations on dialog act (DA) classification on automatically generated transcriptions. We propose a novel approach that combines convolutional neural networks (CNNs) and conditional random fields (CRFs) for context...
Enhancing Temporal Action Localization: Advanced S6 Modeling with Recurrent Mechanism
Temporal Action Localization (TAL) is a critical task in video analysis, identifying precise start and end times of actions. Existing methods like CNNs, RNNs, GCNs, and Transformers have limitations in capturing long-range dependencies and temporal c...
Document Domain Randomization for Deep Learning Document Layout Extraction
We present document domain randomization (DDR), the first successful transfer of convolutional neural networks (CNNs) trained only on graphically rendered pseudo-paper pages to real-world document segmentation. DDR renders pseudo-document pages by mo...
Integrating omics and MRI data with kernel-based tests and CNNs to identify rare genetic m…
For precision medicine and personalized treatment, we need to identify predictive markers of disease. We focus on Alzheimer's disease (AD), where magnetic resonance imaging scans provide information about the disease status. By combining imaging with...
PICO: Pipeline Inference Framework for Versatile CNNs on Diverse Mobile Devices
Distributing the inference of convolutional neural network (CNN) to multiple mobile devices has been studied in recent years to achieve real-time inference without losing accuracy. However, how to map CNN to devices remains a challenge. On the one ha...
Adaptive Convolution for CNN-based Speech Enhancement Models
Deep learning-based speech enhancement methods have significantly improved speech quality and intelligibility. Convolutional neural networks (CNNs) have been proven to be essential components of many high-performance models. In this paper, we introdu...
Transforming Musical Signals through a Genre Classifying Convolutional Neural Network
Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstractin...
Learning Lightweight Lane Detection CNNs by Self Attention Distillation
Training deep models for lane detection is challenging due to the very subtle and sparse supervisory signals inherent in lane annotations. Without learning from much richer context, these models often fail in challenging scenarios, e.g., severe occlu...
LIP: Local Importance-based Pooling
Spatial downsampling layers are favored in convolutional neural networks (CNNs) to downscale feature maps for larger receptive fields and less memory consumption. However, for discriminative tasks, there is a possibility that these layers lose the di...
OSS-Net: Memory Efficient High Resolution Semantic Segmentation of 3D Medical Data
Convolutional neural networks (CNNs) are the current state-of-the-art meta-algorithm for volumetric segmentation of medical data, for example, to localize COVID-19 infected tissue on computer tomography scans or the detection of tumour volumes in mag...
Toward High-Throughput Artificial Intelligence-Based Segmentation in Oncological PET Imagi…
Artificial intelligence (AI) techniques for image-based segmentation have garnered much attention in recent years. Convolutional neural networks (CNNs) have shown impressive results and potential towards fully automated segmentation in medical imagin...
Talking With Your Hands: Scaling Hand Gestures and Recognition With CNNs
The use of hand gestures provides a natural alternative to cumbersome interface devices for Human-Computer Interaction (HCI) systems. As the technology advances and communication between humans and machines becomes more complex, HCI systems should al...
rovalf/breast-cancer-detection-ML-project
An AI-assisted mammogram classification system using Convolutional Neural Networks (CNNs) with Grad-CAM explainability. This project was developed as part of the University of London Final Year Project, with the aim of demonstrating the feasibility of deep learning in improving b…