guo-lf Goto Github PK

adapt

Awesome Domain Adaptation Python Toolbox

awesome-active-learning

A curated list of awesome Active Learning

badge

An implementation of the BADGE batch active learning algorithm.

carla_cil_pytorch

A pytorch implementation to train the conditional imitation learning policy in "Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective".

coiltraine

Training framework for conditional imitation learning

corda

[ICCV 2021] Code for our paper Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation

dassl.pytorch_da_lib

A PyTorch toolbox for domain generalization, domain adaptation and semi-supervised learning.

distil

DISTIL: Deep dIverSified inTeractIve Learning. An active/inter-active learning library built on py-torch for reducing labeling costs.

dual-cross

Cross-Domain and Cross-Modal Knowledge Distillation in Unsupervised Domain Adaptation for 3D Semantic Segmentation(ACM MM 2022)

end2end-multitask-with-attention

explainable-deep-driving

g-vom

G-VOM: A GPU Accelerated Voxel Off-Road Mapping System

iiis-test

learning_research

本人的科研经验

mode-----mixture-of-domain-specific-experts

monodepth2

[ICCV 2019] Monocular depth estimation from a single image

nebula-odometry-dataset

Ready to test your SLAM system in challenging datasets from extreme environments? Try this out! The dataset is provided by the Team CoSTAR that has been intensively testing multi-robot systems in real world environments such as caves, tunnels, abandoned factories and industrial plants for the DARPA Subterranean Challenge.

off-road-benchmark

A New Open-Source Off-road Environment for Benchmark Generalization of Autonomous Driving

off-road-freespace-detection

Official implementation of our ICRA'22 paper: ORFD: A Dataset and Benchmark for Off-Road Freespace Detection

openmixup

CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark

openmmlabcamp-1

pytorch_active_learning

PyTorch Library for Active Learning to accompany Human-in-the-Loop Machine Learning book

sse-xmuda

Self-supervised Exclusive Learning for 3D Segmentation with Cross-Modal Unsupervised Domain Adaptation(ACM MM 2022)

talisman

[ECCV 2022] "TALISMAN: Targeted Active Learning for Object Detection with Rare Classes and Slices using Submodular Mutual Information" by Suraj Kothawade, Saikat Ghosh, Sumit Shekhar, Yu Xiang, Rishabh Iyer

transfer-learning-library

Transfer Learning Library for Domain Adaptation, Task Adaptation, and Domain Generalization

transferlearning

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

xmuda

Cross-Modal Unsupervised Domain Adaptationfor 3D Semantic Segmentation