Overview The dissertation explores the theoretical foundations and practical implications of label blindness in unlabeled out-of-distribution (OOD) detection methods. The main contribution is the ...

Self-Supervised Learning Meets Custom Autoencoder Classifier: A Semi-Supervised Approach for Encrypted Traffic Anomaly Detection ...

These tokens, learned from both labeled and unlabeled data, are fed into the system alongside the image data to shape visual features processing for different contexts. This results in the model ...