13 citations · 29 across the 16 of their papers we have counts for
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FROB: Few-shot ROBust Model for Classification and Out-of-Distribution Detection
Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris
Nowadays, classification and Out-of-Distribution (OoD) detection in the few-shot setting remain challenging aims due to rarity and the limited samples in the few-shot setting, and…
OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary
Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris
Generative models trained in an unsupervised manner may set high likelihood and low reconstruction loss to Out-of-Distribution (OoD) samples. This increases Type II errors and lead…
Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation
Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris
Generative Adversarial Networks (GAN) are a powerful methodology and can be used for unsupervised anomaly detection, where current techniques have limitations such as the accurate…
Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary
Nikolaos Dionelis, Mehrdad Yaghoobi, Sotirios A. Tsaftaris
Generative models, such as Generative Adversarial Networks (GANs), have been used for unsupervised anomaly detection. While performance keeps improving, several limitations exist p…