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Gui Qian's Projects

augmentation_mia icon augmentation_mia

The source code for ICML2021 paper When Does Data Augmentation Help With Membership Inference Attacks?

bi-sampling icon bi-sampling

This is the official PyTorch implementation of the paper "Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning" (Ju He, Adam Kortylewski, Shaokang Yang, Shuai Liu, Cheng Yang, Changhu Wang, Alan Yuille).

darp icon darp

Code for the paper "Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning" (NeurIPS 20)

daso icon daso

A placeholder repository for the paper `Distribution-Aware Semantics-Oriented Pseudo-label Imbalanced Semi-Supervised Learning'.

dive-into-dl-pytorch icon dive-into-dl-pytorch

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet代码实现改为PyTorch实现。

fixmatch icon fixmatch

A simple method to perform semi-supervised learning with limited data.

fixmatch-pytorch icon fixmatch-pytorch

Unofficial PyTorch implementation of "FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence"

gdw-nips2021 icon gdw-nips2021

This repository is the official implementation of Generalized Data Weighting via Class-level Gradient Manipulation (NeurIPS 2021)(http://arxiv.org/abs/2111.00056).

imbalanced-semi-self icon imbalanced-semi-self

[NeurIPS 2020] Semi-Supervision (Unlabeled Data) & Self-Supervision Improve Class-Imbalanced / Long-Tailed Learning

longtail_da icon longtail_da

[CVPR 2020] Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective

ml-visuals icon ml-visuals

🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

pytorch-handbook icon pytorch-handbook

pytorch handbook是一本开源的书籍,目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门,其中包含的Pytorch教程全部通过测试保证可以成功运行

research_resources icon research_resources

Resources of CQU CS 1701 research, include NLP, Knowledge Graph,Cloud Computing, etc.

sdclr icon sdclr

[ICML 2021] “ Self-Damaging Contrastive Learning”, Ziyu Jiang, Tianlong Chen, Bobak Mortazavi, Zhangyang Wang

self-tuning icon self-tuning

Code release for "Self-Tuning for Data-Efficient Deep Learning" (ICML 2021)

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