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
Object classification with CIFAR-10 using transfer learning
Torch implementation of the paper "Deep Pyramidal Residual Networks" (https://arxiv.org/abs/1610.02915).
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.)
edepth is an open-source, trainable CNN-based model for depth estimation from single images, videos, and live camera feeds.
A Benchmark for Semantic Segmentation of Waterbody Images
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
Caffe implementation of the paper "Deep Pyramidal Residual Networks" (https://arxiv.org/abs/1610.02915).
Framework for the automatic creation of CNN architectures
Code for the paper "Curriculum Dropout", ICCV 2017
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