Unofficial PyTorch implementation of Guided Dropout

Overview

Unofficial PyTorch implementation of Guided Dropout

This is a simple implementation of Guided Dropout for research. We try to reproduce the algorithm according to the paper published in AAA-19, but we can't guarantee the performance reported in the paper. We will list some experiment results soon.

TODO

  • Release the reproduced code
  • list experiment results
  • ...

Setup

pip install -r requirements.txt

Run

  1. Run Guided Dropout on CIFAR10 Dataset (mlp 3 hidden layers with 1024 nodes)
python mainpro.py --dataset CIFAR10 --arc mlp --mlp-depth 3 --hidden-dim 1024 -e 200 --lr 0.01 --exp-name mlp-1024-3-guided-dropout-cifar10
  1. Run Original Dropout on Fashionmnist Dataset (mlp 3 hidden layers with 8192 nodes)
python mainpro.py --dataset Fashionmnist --arc mlp --mlp-depth 3 --hidden-dim 8192 -e 200 --lr 0.01 --exp-name mlp-8192-3-original-dropout-cifar10 --drop-type Dropout --drop-rate 0.2
  1. Run Guided Dropout on CIFAR100 Dataset (ResNet-18)
python mainpro.py --dataset CIFAR100 --arc ResNet18 -e 200 --lr 0.01 --exp-name resnet18-guided-dropout-cifar100 --drop-type GuidedDropout --drop-rate 0.2

Result

CIFAR10

Algorithm MLP-1024-3 MLP-2048-3 MLP-4096-3 MLP-8192-3 ResNet18
Non Dropout - - - - -
Original Dropout - - - - -
Guided Dropout (top-k) * 58.75 59.65 59.64 59.92 94.02
Guided Dropout (DR) * 59.84 60.12 60.89 61.32 94.12
Guided Dropout - - - - -
Guided Dropout - - - - -

* means the result listed in the paper

CIFAR100

Algorithm MLP-1024-3 MLP-2048-3 MLP-4096-3 MLP-8192-3 ResNet18
Non Dropout - - - - -
Original Dropout - - - - -
Guided Dropout (top-k) * 30.92 31.59 31.34 32.11 76.98
Guided Dropout (DR) * 31.88 32.78 33.01 33.15 77.52
Guided Dropout - - - - -
Guided Dropout - - - - -
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