implementation of the paper "MarginGAN: Adversarial Training in Semi-Supervised Learning"

Last update: Dec 23, 2022

Related tags

Overview

MarginGAN

This repository is the implementation of the paper "MarginGAN: Adversarial Training in Semi-Supervised Learning".

1."preliminary" is the implementation of Preliminary Experiment on MNIST of the paper. Thank the authors of pytorch-generative-model-collections and examples of pytorch, our code is widely adapted from their repositories.

To train the network, an example is as follows:

python main.py \
  --gan_type MarginGAN \
  --num_labels 600 \
  --lrC 0.1 \
  --epoch 50

2."ablation" is the implementation of Ablation Study on MNIST of the paper. To train the network, an example is as follows:

python main.py \
--gan_type MarginGAN_UG \
--num_labels 600 \
--lrC 0.01 \
--epoch 50

3."further" is the implementation of Experiment on SVHN and CIFAR-10 of the paper. Thank the authors of mean teacher, our code is widely adapted from their repositories.

To train the network, an example is as follows:

python MarginGAN_main.py \
    --dataset cifar10 \
    --train-subdir train+val \
    --eval-subdir test \
    --batch-size 128 \
    --labeled-batch-size 31 \
    --arch cifar_shakeshake26 \
    --consistency-type mse \
    --consistency-rampup 5 \
    --consistency 100.0 \
    --logit-distance-cost 0.01 \
    --weight-decay 2e-4 \
    --lr-rampup 0 \
    --lr 0.05 \
    --nesterov True \
    --labels data-local/labels/cifar10/1000_balanced_labels/00.txt  \
    --epochs 180 \
    --lr-rampdown-epochs 210 \
    --ema-decay 0.97 \
    --generated-batch-size 32

implementation of the paper "MarginGAN: Adversarial Training in Semi-Supervised Learning"

Related tags

Overview

MarginGAN

Owner

Van

DRIFT is a tool for Diachronic Analysis of Scientific Literature.

A parametric soroban written with CADQuery.

PyG (PyTorch Geometric) - A library built upon PyTorch to easily write and train Graph Neural Networks (GNNs)

Scalable Optical Flow-based Image Montaging and Alignment

TorchFlare is a simple, beginner-friendly, and easy-to-use PyTorch Framework train your models effortlessly.

NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework

Mosaic of Object-centric Images as Scene-centric Images (MosaicOS) for long-tailed object detection and instance segmentation.

SAT: 2D Semantics Assisted Training for 3D Visual Grounding, ICCV 2021 (Oral)

Scripts for training an AI to play the endless runner Subway Surfers using a supervised machine learning approach by imitation and a convolutional neural network (CNN) for image classification

Simple Text-Generator with OpenAI gpt-2 Pytorch Implementation

Weakly Supervised Dense Event Captioning in Videos, i.e. generating multiple sentence descriptions for a video in a weakly-supervised manner.

HiFi++: a Unified Framework for Neural Vocoding, Bandwidth Extension and Speech Enhancement

Leaf: Multiple-Choice Question Generation

This is a model made out of Neural Network specifically a Convolutional Neural Network model

Referring Video Object Segmentation

This is the repository of the NeurIPS 2021 paper "Curriculum Disentangled Recommendation withNoisy Multi-feedback"

Keras-1D-NN-Classifier

Multi-Task Deep Neural Networks for Natural Language Understanding

A scanpy extension to analyse single-cell TCR and BCR data.

Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021)