Y-GAN

learning dual data representations for anomaly detection in images

Marija Ivanovska (Avtor), Vitomir Štruc (Avtor)

Povzetek

We propose a novel reconstruction-based model for anomaly detection in image data, called ’Y-GAN’. The model consists of a Y-shaped auto-encoder and represents images in two separate latent spaces. The first captures meaningful image semantics, which are key for representing (normal) training data, whereas the second encodes low-level residual image characteristics. To ensure the dual representations encode mutually exclusive information, a disentanglement procedure is designed around a latent (proxy) classifier. Additionally, a novel representation-consistency mechanism is proposed to prevent information leakage between the latent spaces. The model is trained in a one-class learning setting using only normal training data. Due to the separation of semantically-relevant and residual information, Y-GAN is able to derive informative data representations that allow for efficacious anomaly detection across a diverse set of anomaly detection tasks. The model is evaluated in comprehensive experiments with several recent anomaly detection models using four popular image datasets, i.e., MNIST, FMNIST, CIFAR10, and PlantVillage. Experimental results show that Y-GAN outperforms all tested models by a considerable margin and yields state-of-the-art results. The source code for the model is made publicly available at https://github.com/MIvanovska/Y-GAN.

Ključne besede

detekcija anomalij;enorazredno učenje;globoko učenje;anomaly detection;pone-class learning;deeplearning;

Podatki

Jezik:	Angleški jezik
Leto izida:	2024
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UL FE - Fakulteta za elektrotehniko
UDK:	004.93
COBISS:	184821507
ISSN:	1873-6793
Št. ogledov:	80
Št. prenosov:	33
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Slovenski jezik
Sekundarne ključne besede:	detekcija anomalij;enorazredno učenje;globoko učenje;
Vrsta dela (COBISS):	Članek v reviji
Strani:	17 str.
Zvezek:	ǂVol. ǂ248, [article no.] 123410
Čas izdaje:	15 Avg. 2024
DOI:	10.1016/j.eswa.2024.123410
ID:	23628106