Popular GitHub Repositories for Influence Maps

Image augmentation for machine learning experiments.

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)

High Resolution Depth Maps for Stable Diffusion WebUI

[CVPR'22] ICON: Implicit Clothed humans Obtained from Normals

:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps

?100+ LLM & RL Algorithm Maps！? | 原创 LLM / RL 100+原理图?，由《大模型算法》作者发起

Deeper Depth Prediction with Fully Convolutional Residual Networks (FCRN)

? Images to → 3D Parallax effect video. A free and open source ImmersityAI alternative

Diffusion attentive attribution maps for interpreting Stable Diffusion.

VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models

Predict dense depth maps from sparse and noisy LiDAR frames guided by RGB images. (Ranked 1st place on KITTI) [MVA 2019]

[CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

Geometry-Aware Learning of Maps for Camera Localization (CVPR2018)

使用 flask + pyecharts 搭建的新冠肺炎疫情数据可视化交互分析网站平台，包含疫情数据获取、每日疫情地图、曲线图展示，数据统计分析、态势感知、确诊人数预测分析算法设计、NLP舆情监测等任务（部署在http://covid.yunwei123.tech/）

Pytorch implementation of CVPR2020 paper “VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation”

In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.

User can set up destination for any agent to navigate on Google Map and learn the best route for the agent based on its current condition and the traffic. Our result is 10% less energy consumption than the route provided by Google map

All about attention in neural networks. Soft attention, attention maps, local and global attention and multi-head attention.

TensorFlow implementations of visualization of convolutional neural networks, such as Grad-Class Activation Mapping and guided back propagation