Pinned Repositories
build-your-own-x
Master programming by recreating your favorite technologies from scratch.
CNN_Enhanced_GCN
Q. Liu, L. Xiao, J. Yang and Z. Wei, "CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3037361.
Color-Names
An improved version of the color name model described here: http://lewisandquark.tumblr.com/post/160776374467/new-paint-colors-invented-by-neural-network.
CS6208_2023
Advanced Topics in Artificial Intelligence, NUS CS6208, 2023
datasets
Datasets for deep learning with satellite & aerial imagery
Dubai-Satellite-Imagery-Multiclass-Segmentation
Simulation and performance analysis of 3 benchmark models (Standard U-Net, U-Net with Resnet backbone & U-Net with DeepLabV3+ backbone) for Multiclass Semantic Segmentation of Satellite Images.
foobar41
gcn
Implementation of Graph Convolutional Networks in TensorFlow
gcn_code
A repo for holding example code
gcn_graphsage
Graph Neural Network Tutorial
foobar41's Repositories
foobar41/build-your-own-x
Master programming by recreating your favorite technologies from scratch.
foobar41/CNN_Enhanced_GCN
Q. Liu, L. Xiao, J. Yang and Z. Wei, "CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3037361.
foobar41/Color-Names
An improved version of the color name model described here: http://lewisandquark.tumblr.com/post/160776374467/new-paint-colors-invented-by-neural-network.
foobar41/CS6208_2023
Advanced Topics in Artificial Intelligence, NUS CS6208, 2023
foobar41/datasets
Datasets for deep learning with satellite & aerial imagery
foobar41/Dubai-Satellite-Imagery-Multiclass-Segmentation
Simulation and performance analysis of 3 benchmark models (Standard U-Net, U-Net with Resnet backbone & U-Net with DeepLabV3+ backbone) for Multiclass Semantic Segmentation of Satellite Images.
foobar41/foobar41
foobar41/gcn
Implementation of Graph Convolutional Networks in TensorFlow
foobar41/gcn_code
A repo for holding example code
foobar41/gcn_graphsage
Graph Neural Network Tutorial
foobar41/google-research
Google Research
foobar41/GrainHub
foobar41/GrainHub-final
foobar41/GrainHub-v2
foobar41/MDGCN
Multiscale Dynamic Graph Convolutional Network for hyperspectral image classification
foobar41/graph-rcnn.pytorch
foobar41/GRE_PREP
This is a guide for how one can prepare for GRE within a month's duration.
foobar41/HAT
CVPR2023 - Activating More Pixels in Image Super-Resolution Transformer
foobar41/nitro
NITRO (NITFio, "R" is a ligature for "Fi") is a full-fledged, extensible library solution for reading and writing the National Imagery Transmission Format (NITF), a U.S. DoD standard format. It is written in cross-platform C, with bindings available for other languages.
foobar41/nnfs_book
Sample code from the Neural Networks from Scratch book.
foobar41/Nonlinear-Hyperspectral-Unmixing-Autoencoder
An autoencoder that performs nonlinear pixel unmixing on hyperspectral images (based on the Fan, Bilinear, PPNM models, and also higher order nonlinear terms)
foobar41/Nonlinear-Spectral-Unmixing-with-Auto-Encoder
Implementation of paper "Nonlinear Unmixing of Hyperspectral Data via Deep Autoencoder Networks" (GRSL 2019)
foobar41/pytorch_geometric
Graph Neural Network Library for PyTorch, codes of different architectures in examples
foobar41/RelTR
RelTR: Relation Transformer for Scene Graph Generation: https://arxiv.org/abs/2201.11460v2
foobar41/Semantic-Segment-Anything
Automated dense category annotation engine that serves as the initial semantic labeling for the Segment Anything dataset (SA-1B).
foobar41/SGC
official implementation for the paper "Simplifying Graph Convolutional Networks"
foobar41/sklearn-programs
foobar41/stylegan-encoder
StyleGAN Encoder - converts real images to latent space
foobar41/Tic-Tac-Toe_alpha_beta_prune
A single player Tic Tac Toe game built using React.