Xin Lai's repositories
PyContrast
PyTorch implementation of Contrastive Learning methods; List of awesome-contrastive-learning papers
awesome-semi-supervised-learning
:scroll: An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.
AdaptiveMaskedProxies
Adaptive Masked Proxies for Few Shot Semantic Segmentation
Awesome-Learning-with-Label-Noise
A curated list of resources for Learning with Noisy Labels
awesome_OpenSetRecognition_list
A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition
CloserLookFewShot
source code to ICLR'19, 'A Closer Look at Few-shot Classification'
detectron2
Detectron2 is FAIR's next-generation research platform for object detection and segmentation.
few-shot-object-detection
Implementations of few-shot object detection benchmarks
GitWorkshop
This is for course workshop.
google-research
Google Research
HIT-WorkShop
HIT HomeWorks or Templets
HowToLiveLonger
程序员延寿指南 | A programmer's guide to live longer
indexnet_matting
Indices Matter: Learning to Index for Deep Image Matting
maml
Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"
mentornet
Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks
meta-transfer-learning
TensorFlow and PyTorch implementations of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)
mmdetection
Open MMLab Detection Toolbox and Benchmark
NLNL-Negative-Learning-for-Noisy-Labels
NLNL: Negative Learning for Noisy Labels
paper-reading
深度学习论文阅读、数据仓库实践体验。比做算法的懂工程落地,比做工程的懂算法模型。
pytorch-mobilenet-v2
A PyTorch implementation of MobileNet V2 architecture and pretrained model.
RepMet
Few-shot detection for visual categories
sam
SAM: Sharpness-Aware Minimization (PyTorch)
setup-ipsec-vpn
Scripts to build your own IPsec VPN server, with IPsec/L2TP and Cisco IPsec on Ubuntu, Debian and CentOS
transferlearning
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习