dfm

Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection

This is the implementation of DFM paper.

Model Type: Classification

Description

Fast anomaly classification algorithm that consists of a deep feature extraction stage followed by anomaly classification stage consisting of PCA and class-conditional Gaussian Density Estimation.

Feature Extraction

Features are extracted by feeding the images through a ResNet18 backbone, which was pre-trained on ImageNet. The output of the penultimate layer (average pooling layer) of the network is used to obtain a semantic feature vector with a fixed length of 2048.

Anomaly Detection

In the anomaly classification stage, class-conditional PCA transformations and Gaussian Density models are learned. Two types of scores are calculated (i) Feature-reconstruction scores (norm of the difference between the high-dimensional pre-image of a reduced dimension feature and the original high-dimensional feature), and (ii) Negative log-likelihood under the learnt density models. Either of these scores can be used for anomaly detection.

Usage

python tools/train.py --model dfm

Benchmark

All results gathered with seed 42.

MVTec AD Dataset

Image-Level AUC

	Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
ResNet-18	0.894	0.864	0.558	0.945	0.984	0.946	0.994	0.913	0.871	0.979	0.941	0.838	0.761	0.95	0.911	0.949
Wide ResNet-50	0.891	0.978	0.540	0.979	0.977	0.974	0.990	0.891	0.931	0.947	0.839	0.809	0.700	0.911	0.915	0.981

Image F1 Score

	Avg	Carpet	Grid	Leather	Tile	Wood	Bottle	Cable	Capsule	Hazelnut	Metal Nut	Pill	Screw	Toothbrush	Transistor	Zipper
ResNet-18	0.919	0.895	0.844	0.926	0.971	0.948	0.977	0.874	0.935	0.957	0.958	0.921	0.874	0.933	0.833	0.943
Wide ResNet-50	0.951	0.960	0.844	0.990	0.970	0.959	0.976	0.848	0.944	0.913	0.912	0.919	0.859	0.893	0.815	0.961