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IPAD: Intensity Potential for Adaptive De-Quantization.

, , , , , and . IEEE Trans. Image Process., 27 (10): 4860-4872 (2018)

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Iterative Metric Learning for Imbalance Data Classification., , , and . IJCAI, page 2805-2811. ijcai.org, (2018)Emotion Recognition by Edge-Weighted Hypergraph Neural Network., , , , and . ICIP, page 2144-2148. IEEE, (2019)Hypergraph-Guided Disentangled Spectrum Transformer Networks for Near-Infrared Facial Expression Recognition., , , , , and . AAAI, page 10101-10109. AAAI Press, (2024)A novel fault diagnosis mechanism for wireless sensor networks., , , , , and . Math. Comput. Model., 54 (1-2): 330-343 (2011)Directional diagnosis for wireless sensor networks., , , , and . DCOSS, page 1-8. IEEE Computer Society, (2011)GLADformer: A Mixed Perspective for Graph-Level Anomaly Detection., , , , , , , , , and . ECML/PKDD (6), volume 14946 of Lecture Notes in Computer Science, page 337-353. Springer, (2024)xASTNN: Improved Code Representations for Industrial Practice., , , , , and . CoRR, (2023)IPAD: Intensity Potential for Adaptive De-Quantization., , , , , and . IEEE Trans. Image Process., 27 (10): 4860-4872 (2018)GVCNN: Group-View Convolutional Neural Networks for 3D Shape Recognition., , , , and . CVPR, page 264-272. Computer Vision Foundation / IEEE Computer Society, (2018)DRLS: A Deep Reinforcement Learning Based Scheduler for Time-Triggered Ethernet., , , and . ICCCN, page 1-11. IEEE, (2021)