The code for our paper Semi-Supervised Learning with Multi-Head Co-Training

Overview

Semi-Supervised Learning with Multi-Head Co-Training (PyTorch)

Abstract

Co-training, extended from self-training, is one of the frameworks for semi-supervised learning. Without natural split of features, single-view co-training works at the cost of training extra classifiers, where the algorithm should be delicately designed to prevent individual classifiers from collapsing into each other. To remove these obstacles which deter the adoption of single-view co-training, we present a simple and efficient algorithm Multi-Head Co-Training. By integrating base learners into a multi-head structure, the model is in a minimal amount of extra parameters. Every classification head in the unified model interacts with its peers through a “Weak and Strong Augmentation” strategy, in which the diversity is naturally brought by the strong data augmentation. Therefore, the proposed method facilitates single-view co-training by 1). promoting diversity implicitly and 2). only requiring a small extra computational overhead. The effectiveness of MultiHead Co-Training is demonstrated in an empirical study on standard semi-supervised learning benchmarks.

Experiments

First, please make sure your pytorch version is above 1.6. Then run the train.py, such as

$ python train.py --num_labels 4000 --save_name cifar10_4000 --dataset cifar10 --overwrite --data_dir path-to-your-data

Reference

Most of codes in this repository are modified from: "https://github.com/google-research/fixmatch", "https://github.com/LeeDoYup/FixMatch-pytorch", "https://github.com/ildoonet/pytorch-randaugment".

Official code for the paper "Self-Supervised Prototypical Transfer Learning for Few-Shot Classification"

Self-Supervised Prototypical Transfer Learning for Few-Shot Classification This repository contains the reference source code and pre-trained models (

EPFL INDY 44 Nov 04, 2022
Graph Analysis From Scratch

Graph Analysis From Scratch Goal In this notebook we wanted to implement some functionalities to analyze a weighted graph only by using algorithms imp

Arturo Ghinassi 0 Sep 17, 2022
This provides the R code and data to replicate results in "The USS Trustee’s risky strategy"

USSBriefs2021 This provides the R code and data to replicate results in "The USS Trustee’s risky strategy" by Neil M Davies, Jackie Grant and Chin Yan

1 Oct 30, 2021
👨‍💻 run nanosaur in simulation with Gazebo/Ingnition

🦕 👨‍💻 nanosaur_gazebo nanosaur The smallest NVIDIA Jetson dinosaur robot, open-source, fully 3D printable, based on ROS2 & Isaac ROS. Designed & ma

nanosaur 9 Jul 19, 2022
Implementation of "Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification"

hypergraph_reid Implementation of "Learning Multi-Granular Hypergraphs for Video-Based Person Re-Identification" If you find this help your research,

62 Dec 21, 2022
PyTorch Implementation of Exploring Explicit Domain Supervision for Latent Space Disentanglement in Unpaired Image-to-Image Translation.

DosGAN-PyTorch PyTorch Implementation of Exploring Explicit Domain Supervision for Latent Space Disentanglement in Unpaired Image-to-Image Translation

40 Nov 30, 2022
An experiment to bait a generalized frontrunning MEV bot

Honeypot 🍯 A simple experiment that: Creates a honeypot contract Baits a generalized fronturnning bot with a unique transaction Analyze bot behaviour

0x1355 14 Nov 24, 2022
BookMyShowPC - Movie Ticket Reservation App made with Tkinter

Book My Show PC What is this? Movie Ticket Reservation App made with Tkinter. Tk

The Nithin Balaji 3 Dec 09, 2022
Robust fine-tuning of zero-shot models

Robust fine-tuning of zero-shot models This repository contains code for the paper Robust fine-tuning of zero-shot models by Mitchell Wortsman*, Gabri

224 Dec 29, 2022
YOLOX-RMPOLY

本算法为适应robomaster比赛,而改动自矩形识别的yolox算法。 基于旷视科技YOLOX,实现对不规则四边形的目标检测 TODO 修改onnx推理模型 更改/添加标注: 1.yolox/models/yolox_polyhead.py: 1.1继承yolox/models/yolo_

3 Feb 25, 2022
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs

Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs This is an implemetation of the paper Few-shot Relation Extraction via Baye

MilaGraph 36 Nov 22, 2022
Attempt at implementation of a simple GAN using Keras

Simple GAN This is my attempt to make a wrapper class for a GAN in keras which can be used to abstract the whole architecture process. Simple GAN Over

Deven96 7 May 23, 2019
PyTorch implementation of U-TAE and PaPs for satellite image time series panoptic segmentation.

Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks (ICCV 2021) This repository is the official implem

71 Jan 04, 2023
(CVPR 2022 Oral) Official implementation for "Surface Representation for Point Clouds"

RepSurf - Surface Representation for Point Clouds [CVPR 2022 Oral] By Haoxi Ran* , Jun Liu, Chengjie Wang ( * : corresponding contact) The pytorch off

Haoxi Ran 264 Dec 23, 2022
NAACL'2021: Factual Probing Is [MASK]: Learning vs. Learning to Recall

OptiPrompt This is the PyTorch implementation of the paper Factual Probing Is [MASK]: Learning vs. Learning to Recall. We propose OptiPrompt, a simple

Princeton Natural Language Processing 150 Dec 20, 2022
PyTorch framework, for reproducing experiments from the paper Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks

Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks. Code, based on the PyTorch framework, for reprodu

Asaf 3 Dec 27, 2022
Apollo optimizer in tensorflow

Apollo Optimizer in Tensorflow 2.x Notes: Warmup is important with Apollo optimizer, so be sure to pass in a learning rate schedule vs. a constant lea

Evan Walters 1 Nov 09, 2021
This repo provides code for QB-Norm (Cross Modal Retrieval with Querybank Normalisation)

This repo provides code for QB-Norm (Cross Modal Retrieval with Querybank Normalisation) Usage example python dynamic_inverted_softmax.py --sims_train

36 Dec 29, 2022
A face dataset generator with out-of-focus blur detection and dynamic interval adjustment.

A face dataset generator with out-of-focus blur detection and dynamic interval adjustment.

Yutian Liu 2 Jan 29, 2022
[ICCV'2021] Image Inpainting via Conditional Texture and Structure Dual Generation

[ICCV'2021] Image Inpainting via Conditional Texture and Structure Dual Generation

Xiefan Guo 122 Dec 11, 2022