README.md

RaPP

RaPP: Novelty Detection with Reconstruction along Projection Pathway, Ki Hyun Kim, Sangwoo Shim, Yongsub Lim, Jongseob Jeon, Jeongwoo Choi, Byungchan Kim and Andre S. Yoon, ICLR, 2020 paper

Result

Table 2 is the result from paper.

And below is the result of our code.

model	auroc	sap_auroc	nap_auroc
ae	0.858531	0.908304	0.916463
vae	0.880025	0.929843	0.873222
aae	0.862623	0.911775	0.916441

You can see the each experiment result in assets/runs.csv

Usage

Environment

• python==3.8
• pytorch-lightning==1.2.10
• torch==1.8.1
• torchmetrics==0.2.0
• torchvision==0.9.1
• mlflow==1.15.0
• scikit-learn==0.24.1

pip install -r requirements.txt

Run

Default run is multimodal setup.

python src/train.py --target_label 0

To run unimodal setup use --unimodal argument.

python src/train.py --target_label 0 --unimodal

For more information with argument, see below section.

Arguments

dataset

Only conducted experiments with MNIST dataset.

unimodal

There are two type of using MNIST dataset. From paper, explains like below.

1. Multimodal Normality: A single class is chosen to be the novelty class and the remaining classes are assigned as the normal class. This setup is repeated to produce sub-datasets with all possible novelty assignments. For instance, MNIST results in a set of datasets with 10 different novelty classes.
2. Unimodal Normality: In contrast to the multimodal normality setup, we take one class for normality, and the others for novelty. For instance, MNIST results in a set of datasets with 10 different normal classes

taget label

Target label differs from multimodal, unimodal setup. In multimodal setup, target label means unseen label. On the other hand, in unimodal setup, target label means seen label.

rapp_start_index & rapp_end_index

From figure 1 in paper, each encoder layer generates score. We need to decide to use which layer's score. For example, if encoder has 10 layers length of score should be 10. Below is pseudo code for understand.

layer_score = []
for encoder in encoder_layer:
    x = encoder(x)
    recon_x = encoder(recon_x)
    score = (x - recon_x) ** 2
    layer_score.append(score)

With given rapp_start_index and rapp_end_index, we slice the layer_score to get sap_score and nap_score.

layer_score = layer_score[rapp_start_index:rapp_end_index]