MyeongJin-Kim

MyeongJin-Kim's Stars

• facebook/prophet

  Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

  Language:Python18.7k4532.2k4.6k

• ourownstory/neural_prophet

  NeuralProphet: A simple forecasting package

  Language:Python3.9k58552483

• thuml/Transfer-Learning-Library

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

  Language:Python3.5k49206562

• Tramac/awesome-semantic-segmentation-pytorch

  Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)

  Language:Python2.9k42203586

• he-y/Awesome-Pruning

  A curated list of neural network pruning resources.

  2.4k8828328

• sthalles/SimCLR

  PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations

  Language:Jupyter Notebook2.3k2158466

• haitongli/knowledge-distillation-pytorch

  A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility

  Language:Python1.9k2025346

• yassouali/awesome-semi-supervised-learning

  😎 An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.

  1.8k6216228

• AberHu/Knowledge-Distillation-Zoo

  Pytorch implementation of various Knowledge Distillation (KD) methods.

  Language:Python1.6k2317269

• ZhiningLiu1998/awesome-imbalanced-learning

  😎 Everything about class-imbalanced/long-tail learning: papers, codes, frameworks, and libraries | 有关类别不平衡/长尾学习的一切：论文、代码、框架与库

  1.4k446227

• onlybooks/python-algorithm-interview

  <파이썬 알고리즘 인터뷰> 95가지 알고리즘 문제 풀이로 완성하는 코딩 테스트

  Language:Python1.2k25163326

• CVPR2023-3D-Occupancy-Prediction/CVPR2023-3D-Occupancy-Prediction

  CVPR2023-Occupancy-Prediction-Challenge

  Language:Python808176261

• OpenDriveLab/OccNet

  [ICCV 2023] OccNet: Scene as Occupancy

  Language:Python577164952

• yassouali/CCT

  :page_facing_up: Semi-Supervised Semantic Segmentation with Cross-Consistency Training (CVPR 2020).

  Language:Python396106857

• sjhwang82/AdvancedML

  Reading list for the Advanced Machine Learning Course

  36818071

• facebookresearch/DeeperCluster

  Implements the unsupervised pre-training of convolutional neural networks

  Language:Python249131833

• danielgy/Paper-list-on-Imbalanced-Time-series-Classification-with-Deep-Learning

  18515046

• linusericsson/ssl-transfer

  Official code for the CVPR 2021 paper "How Well Do Self-Supervised Models Transfer?"

  Language:Python1818929

• alinlab/Confident_classifier

  Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples / ICLR 2018

  Language:Python17911647

• Britefury/cutmix-semisup-seg

  Semi-supervised semantic segmentation needs strong, varied perturbations

  Language:Jupyter Notebook1634923

• thuml/A-Roadmap-for-Transfer-Learning

  160608

• biomedia-mira/masf

  Domain Generalization via Model-Agnostic Learning of Semantic Features

  Language:Python14671618

• glefundes/mobile-face-gaze

  Lightweight gaze estimation with PyTorch.

  Language:Jupyter Notebook954619

• stu92054/Domain-adaptation-on-segmentation

  Language:Python70318

• mlvlab/SELAR

  Official PyTorch Implementation of "Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs". NeurIPS 2020.

  Language:Python534612

• johncai117/Meta-Fine-Tuning

  [CVPR 2020 VL3] The repository for meta fine-tuning in cross-domain few-shot learning.

  Language:Python41203

• wandb/gitbook

  Documentation synced with GitBook. For all issues with the wandb library, please use https://github.com/wandb/client/issues

  Language:JavaScript417026

• MadryLab/data-transfer

  Language:Python34531

• IST-DASLab/sparse-imagenet-transfer

  Code for reproducing the results in "How Well do Sparse Imagenet Models Transfer?", presented at CVPR 2022

  Language:Python8600

• Saehyung-Lee/OAT

  This repository is the official implementation of "Removing Undesirable Feature Contributions using Out-of-Distribution Data", published as a conference paper at ICLR 2021.

  Language:Python8122