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Using Deep Convolutional Neural Network Architectures for Object Classification and Detection Within X-Ray Baggage Security Imagery., , , and . IEEE Trans. Inf. Forensics Secur., 13 (9): 2203-2215 (2018)Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection, , and . (2019)cite arxiv:1901.08954Comment: Conference Submission. 8 pages, 9 figures.Multi-Task Regression-based Learning for Autonomous Unmanned Aerial Vehicle Flight Control within Unstructured Outdoor Environments., , , , and . CoRR, (2019)Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled Ensemble., , , and . CoRR, (2024)Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation., , and . IV, page 1413-1420. IEEE, (2021)On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery., , , , and . ICMLA, page 986-991. IEEE, (2019)Skip-GANomaly: Skip Connected and Adversarially Trained Encoder-Decoder Anomaly Detection, , and . 2019 International Joint Conference on Neural Networks (IJCNN), (July 2019)Multi-Task Regression-Based Learning for Autonomous Unmanned Aerial Vehicle Flight Control Within Unstructured Outdoor Environments., , , , and . IEEE Robotics Autom. Lett., 4 (4): 4116-4123 (2019)GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training, , and . (2018)A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items., , , , and . CoRR, (2022)