Preprocess file of the dataset used in implicit sub-populations:(Demographic groups: race and gender) The following code will pre-process the jigsaw dataset and return train/test dataset files including demographic groups information. Step-1: Download the jigsaw dataset: identity_individual_annotations.csv from https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data. Step-2: python preprocecss_jiasaw_toxicity_gender_and_race_balanced.py Implementation of SSL methods Please follow the official implementations of MixMatch, MixText, and UDA. [1] https://github.com/google-research/mixmatch [2] https://github.com/GT-SALT/MixText [3] https://github.com/google-research/uda GitHub – UCSC-REAL/Disparate-SSL at pythonawesome.com Contribute to UCSC-REAL/Disparate-SSL development by creating an account on GitHub. GitHubUCSC-REAL    

By Mengde Xu*, Zheng Zhang*, Han Hu, Jianfeng Wang, Lijuan Wang, Fangyun Wei, Xiang Bai, Zicheng Liu. This repo is the official implementation of ICCV2021 paper “End-to-End Semi-Supervised Object Detection with Soft Teacher”. Citation

Neural-Semi-Supervised-Learning-for-Text-Classification Neural Semi supervised Learning for Text Classification Under Large Scale Pretraining. Download Models and Dataset Datasets and Models are found in the follwing list. Download 3.4M IMDB movie reviews. Save the data at [REVIEWS_PATH].You can download the dataset HERE. Download the vanilla RoBERTa-large model released by HuggingFace. Save the model at [VANILLA_ROBERTA_LARGE_PATH].You can download the model HERE. Download in-domain pretrained models in the paper and save the model at [PRETRAIN_MODELS]. We provide three following models.You can download HERE. init-roberta-base: […]

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CAC) Xin Lai*, Zhuotao Tian*, Li Jiang, Shu Liu, Hengshuang Zhao, Liwei Wang, Jiaya Jia This is the official PyTorch implementation of our paper Semi-supervised Semantic Segmentation with Directional Context-aware Consistency that has been accepted to 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021). [Paper] Our method achives the state-of-the-art performance on semi-supervised semantic segmentation. Based on CCT, this Repository also supports efficient distributed training with multiple GPUs. Environment The […]