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

Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.

Rename Images with Auto Generated Neural Image Captions

This is the official implementation of TrivialAugment and a mini-library for the application of multiple image augmentation strategies including RandAugment and TrivialAugment.

Training BERT with Compute/Time (Academic) Budget

Code for ICML 2021 paper: How could Neural Networks understand Programs?

The Curious Layperson: Fine-Grained Image Recognition without Expert Labels (BMVC 2021)

DeepMoCap: Deep Optical Motion Capture using multiple Depth Sensors and Retro-reflectors

This is an official implementation for the WTW Dataset in "Parsing Table Structures in the Wild " on table detection and table structure recognition.

The official repo of the CVPR 2021 paper Group Collaborative Learning for Co-Salient Object Detection .

LogAvgExp - Pytorch Implementation of LogAvgExp

Large scale and asynchronous Hyperparameter Optimization at your fingertip.

Framework for evaluating ANNS algorithms on billion scale datasets.

Two-stage CenterNet

HMLET (Hybrid-Method-of-Linear-and-non-linEar-collaborative-filTering-method)

Generative vs Discriminative: Rethinking The Meta-Continual Learning (NeurIPS 2021)

SOTR: Segmenting Objects with Transformers [ICCV 2021]

SmallInitEmb - LayerNorm(SmallInit(Embedding)) in a Transformer to improve convergence

Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.

This repository comes with the paper "On the Robustness of Counterfactual Explanations to Adverse Perturbations"

Learning to Stylize Novel Views