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annotated_deep_learning_paper_implementations

🧑‍🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

bcdm

[AAAI 2021] Code release for "Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation" https://arxiv.org/abs/2012.06995

cd-fer-benchmark

Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition (AGRA, ACM 2020, Oral)

cglrl

coursedesign-os

操作系统的课程设计

data-cshsi

Open source datasets for Cross-Scene Hyperspectral Image Classification, continuously updated~

deep-learning-teaching

Deep learning courses for postgraduates

guiding-pseudo-labels-with-uncertainty-estimation-for-source-free-unsupervised-domain-adaptation

Code for "Guiding-Pseudo-labels-with-Uncertainty-Estimation-for-Source-free-Unsupervised-Domain-Adaptation" (CVPR 2023)

ieee_tip_sdenet

Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification, IEEE TIP, 2023.

ieee_tnnls_tstnet

Topological Structure and Semantic Information Transfer Network for Cross-Scene Hyperspectral Image Classification. IEEE TNNLS, 2021.

mcd_da

pythonpark

Python 开源项目之「自学编程之路」，保姆级教程：AI实验室、宝藏视频、数据结构、学习指南、机器学习实战、深度学习实战、网络爬虫、大厂面经、程序人生、资源分享。

res-cp

Semi-Supervised Hyperspectral Image Classification

resnet50incub200

resnet50code

semi-supervised-learning

A Unified Semi-Supervised Learning Codebase (NeurIPS'22)

tlbook-code

Code for Transfer Learning book--《迁移学习导论》配套代码

torchssl

A PyTorch-based library for semi-supervised learning (NeurIPS'21)

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

tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.

uda

Unsupervised Domain Adaptation for Computer Vision Tasks