hmi88 / what

Pytorch implementation of "What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?"

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

Pytorch implementation of "What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?", NIPS 2017

1. Usage

# Data Tree
config.data_dir/
└── config.data_name/

# Project Tree
WHAT
├── WHAT_src/
│       ├── data/ *.py
│       ├── loss/ *.py
│       ├── model/ *.py
│       └── *.py
└── WHAT_exp/
         ├── log/
         ├── model/
         └── save/         

1.1 Train

# L2 loss only 
python train.py --uncertainty "normal" --drop_rate 0.

# Epistemic / Aleatoric 
python train.py --uncertainty ["epistemic", "aleatoric"]

# Epistemic + Aleatoric
python train.py --uncertainty "combined"

1.2 Test

# L2 loss only 
python train.py --is_train false --uncertainty "normal"

# Epistemic
python train.py --is_train false --uncertainty "epistemic" --n_samples 25 [or 5, 50]

# Aleatoric
python train.py --is_train false --uncertainty "aleatoric" 

# Epistemic + Aleatoric
python train.py --is_train false --uncertainty "combined" --n_samples 25 [or 5, 50]

1.3 Requirements

• Python3.7

• Pytorch >= 1.0

• Torchvision

• distutils

2. Experiment

This is not official implementation.

2.1 Network & Datset

• Autoencoder based on Bayesian Segnet

  • Network depth 2 (paper 5)
  • Drop_rate 0.2 (paper 0.5)

• Fahsion MNIST / MNIST

  • Input = Label (for autoencoder)

2.2 Results

2.2.1 PSNR

Combined > Aleatoric > Normal (w/o D.O) > Epistemic > Normal (w/ D.O)

2.2.2 Images

Input / Label

Combined

Aleatoric

Epistemic