Daeun Lee's repositories
awesome-pedestrian-trajectory
🚶♀️Awesome repository for pedestrian trajectory prediction
For_codingtest
Algorithm archive for future 🔥
Algorithms
자료구조, 알고리즘 공부(with python)
awesome-autonomous-pointcloud
🚙 Awesome Pointcloud 3D detection in autonomous driving
ai-tech-interview
👩💻👨💻 AI 엔지니어 기술 면접 스터디
daeunni.github.io
AcadHomepage: A Modern and Responsive Academic Personal Homepage
DG-Colorization
The official PyTorch implementation of "Bridging the Domain Gap towards Generalization in Automatic Colorization", [ECCV 2022].
KED_Project
KED(한국기업데이터) 분석공모전 (우수상, 2위)
All-about-R-
R의 기초적인 문법부터 활용까지를 정리한 repository 입니다.
auto-lambda
The Implementation of "Auto-Lambda: Disentangling Dynamic Task Relationships".
AuxiLearn
Official implementation of Auxiliary Learning by Implicit Differentiation [ICLR 2021]
Awesome-Incremental-Learning
Awesome Incremental Learning
Basic_matlab
Numerical analysis and computer experiments(MATH342)
CLIP
Contrastive Language-Image Pretraining
CNN_PyTorch-codes
CNN 기반 모델 codes 모음 (PyTorch)
daeun_resume
A modern static resume template and theme. Powered by Jekyll and GitHub pages.
DomainBed
DomainBed is a suite to test domain generalization algorithms
kitti_object_vis
KITTI Object Visualization (Birdview, Volumetric LiDar point cloud )
l5kit
L5Kit - https://level-5.global/
Mix-of-Show
NeurIPS 2023, Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models
OpenTraj
Human Trajectory Prediction Dataset Benchmark (ACCV 2020)
pytorch-auto-drive
PytorchAutoDrive: Segmentation models (ERFNet, ENet, DeepLab, FCN...) and Lane detection models (SCNN, RESA, LSTR, LaneATT, BézierLaneNet...) based on PyTorch with fast training, visualization, benchmarking & deployment help
Quant-Analysis
퀀트 관련 금융 data 분석 (in KQR)
Self-Driving-Projects-
Self-driving projects ( Detection, ADAS )
sleep_classifiers
Classify sleep from heart rate and acceleration via Apple Watch
Statistical_ML
(20-2) Statistical Machine Learning
Unifying_to_Cityscapes_labels
Make various driving dataset's segmentation labels to the Cityscapes format (For Domain Adaptation)