  Incorporating human perception into training of convolutional neural networks
(CNN) has boosted generalization capabilities of such models in open-set
recognition tasks. One of the active research questions is where (in the model
architecture) and how to efficiently incorporate always-limited human
perceptual data into training strategies of models. In this paper, we introduce
MENTOR (huMan pErceptioN-guided preTraining fOr increased geneRalization),
which addresses this question through two unique rounds of training the CNNs
tasked with open-set anomaly detection. First, we train an autoencoder to learn
human saliency maps given an input image, without class labels. The autoencoder
is thus tasked with discovering domain-specific salient features which mimic
human perception. Second, we remove the decoder part, add a classification
layer on top of the encoder, and fine-tune this new model conventionally. We
show that MENTOR's benefits are twofold: (a) significant accuracy boost in
anomaly detection tasks (in this paper demonstrated for detection of unknown
iris presentation attacks, synthetically-generated faces, and anomalies in
chest X-ray images), compared to models utilizing conventional transfer
learning (e.g., sourcing the weights from ImageNet-pretrained models) as well
as to models trained with the state-of-the-art approach incorporating human
perception guidance into loss functions, and (b) an increase in the efficiency
of model training, requiring fewer epochs to converge compared to
state-of-the-art training methods.
