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a2s2k-resnet

A2S2K-ResNet: Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification

awesome-gnn-research

My future research

awesome-java

Collection of awesome Java project on Github(非常棒的 Java 开源项目集合).

awesome-spider

爬虫集合

awesome-visual-transformer

Collect some papers about transformer with vision. Awesome Transformer with Computer Vision (CV)

colossalai

Making big AI models cheaper, easier, and scalable

deep-learning-methods-for-hyperspectral-image-denoising

Deep learning methods for Hyperspectral Image Denoising

deeplearning4j

Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.

dive-into-dl-pytorch

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

google-research

Google Research

hyde

leetcode-master

LeetCode 刷题攻略：200道经典题目刷题顺序，共60w字的详细图解，视频难点剖析，50余张思维导图，支持C++，Java，Python，Go，JavaScript等多语言版本，从此算法学习不再迷茫！🔥🔥 来看看，你会发现相见恨晚！🚀

machine_learing_study

myfirst

helloworld

nn-cuda-example

Several simple examples for popular neural network toolkits calling custom CUDA operators.

ruoyi-vue-pro

基于SpringBoot，Spring Security，JWT，Vue & Element 的前后端分离权限管理系统

spectralnet

Hyperspectral Image (HSI) classification using Convolutional Neural Networks (CNN) is widely found in the current literature. Approaches vary from using SVMs to 2D CNNs, 3D CNNs, 3D-2D CNNs. Besides 3D-2D CNNs and FuSENet, the other approaches do not consider both the spectral and spatial features together for HSI classification task, thereby resulting in poor performances. 3D CNNs are computationally heavy and are not widely used, while 2D CNNs do not consider multi-resolution processing of images, and only limits itself to the spatial features. Even though 3D-2D CNNs try to model the spectral and spatial features their performance seems limited when applied over multiple dataset. In this article, we propose SpectralNET, a wavelet CNN, which is a variation of 2D CNN for multi-resolution HSI classification. A wavelet CNN uses layers of wavelet transform to bring out spectral features. Computing a wavelet transform is lighter than computing 3D CNN. The spectral features extracted are then connected to the 2D CNN which bring out the spatial features, thereby creating a spatial-spectral feature vector for classification. Overall a better model is achieved that can classify multi-resolution HSI data with high accuracy. Experiments performed with SpectralNET on benchmark dataset, i.e. Indian Pines, University of Pavia, and Salinas Scenes confirm the superiority of proposed SpectralNET with respect to the state-of-the-art methods.

torch

transportationnetworks

Transportation Networks for Research