Skip-GANomaly++: Skip Connections and Residual Blocks for Anomaly Detection (Student Abstract) - Park, June-Young; Hong, Jae-Ryung; Kim, Min-Hye; Kim, Tae-Joon (Proceedings of the AAAI Conference on Artificial Intelligence, 2024)
Anomaly detection is a critical task across various domains. Fundamentally, anomaly detection models offer methods to identify unusual patterns that do not align with expected behaviors. Notably, in the medical field, detecting anomalies in medical imagery or biometrics can facilitate early diagnosis of diseases. Consequently, we propose the Skip-GANomaly++ model, an enhanced and more efficient version of the conventional anomaly detection models. The proposed model’s performance was evaluated through comparative experiments. Experimental results demonstrated superior performance across most classes compared to the previous models.