DexiNed: Dense EXtreme Inception Network for Edge Detection
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Updated
Mar 8, 2023 - Python
DexiNed: Dense EXtreme Inception Network for Edge Detection
real-time fire detection in video imagery using a convolutional neural network (deep learning) - from our ICIP 2018 paper (Dunnings / Breckon) + ICMLA 2019 paper (Samarth / Bhowmik / Breckon)
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation Pytorch's Implement
Torch implementation of the paper "Deep Pyramidal Residual Networks" (https://arxiv.org/abs/1610.02915).
Object classification with CIFAR-10 using transfer learning
This repository is the official release of the code for the following paper "FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture" which is published at the 13th Asian Conference on Computer Vision (ACCV 2016).
Hands-On Deep Learning Algorithms with Python, By Packt
Joint scene classification and semantic segmentation with FuseNet
A Fast Dense Spectral-Spatial Convolution Network Framework for Hyperspectral Images Classification(Remote Sensing 2018)
DVDnet: A Simple and Fast Network for Deep Video Denoising
"LipNet: End-to-End Sentence-level Lipreading" in PyTorch
Deep Learning code
An Image classifier to identify whether the given image is Batman or Superman using a CNN with high accuracy. (From getting images from google to saving our trained model for reuse.)
A Benchmark for Semantic Segmentation of Waterbody Images
edepth is an open-source, trainable CNN-based model for depth estimation from single images, videos, and live camera feeds.
This Repository is for the MISA Course final project which was Brain tissue segmentation. we adopt NeuroNet which is a comprehensive brain image segmentation tool based on a novel multi-output CNN architecture which has been trained and tuned using IBSR18 dataset
Framework for the automatic creation of CNN architectures
Caffe implementation of the paper "Deep Pyramidal Residual Networks" (https://arxiv.org/abs/1610.02915).
Code for the paper "Curriculum Dropout", ICCV 2017
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