Research
Selected papers of my research grouped into research topics
Deep Knowledge Distillation
- M. Tzelepi, C. Symeonidis, N. Nikolaidis and A. Tefas, “Multilayer Online Self-Acquired Knowledge Distillation”, 26th International Conference on Pattern Recognition (ICPR), 2022
- M. Tzelepi, N. Passalis and A. Tefas, “Probabilistic Online Self-Distillation”, Neurocomputing, 2022
- M. Tzelepi and A. Tefas, “Efficient Training of Lightweight Neural Networks Using Online Self-Acquired Knowledge Distillation”, IEEE International Conference on Multimedia and Expo (ICME), 2021
- M. Tzelepi, N. Passalis and A. Tefas, “Online Subclass Knowledge Distillation”, Expert Systems with Applications, 2021
Deep Representation Learning and Retrieval
- D. Moralis, M. Tzelepi and A. Tefas, “Retrieval-based methodology for few-sample logo recognition”, IEEE International Workshop on Multimedia Signal Processing (MMSP), 2023
- C. Nasioutzikis, M. Tzelepi and A. Tefas, “Deep Hashing Regularization Towards Hamming Space Retrieval”, 11th Hellenic Conference on Artificial Intelligence (SETN), 2020
- M. Tzelepi and A. Tefas, “Deep Convolutional Learning for Content Based Image Retrieval”, Neurocomputing, 2018
- N. Kondylidis, M. Tzelepi and A. Tefas, “Exploiting tf-idf in Deep Convolutional Neural Networks for Content Based Image Retrieval”, Multimedia Tools and Applications, 2018
- M. Tzelepi and A. Tefas, “Deep Convolutional Image Retrieval: A General Framework”, Signal Processing: Image Communication, 2018
- M. Tzelepi and A. Tefas, “Exploiting supervised learning for finetuning deep CNNs in Content Based Image Retrieval”, International Conference on Pattern Recognition (ICPR), 2016
- M. Tzelepi and A. Tefas, “Relevance Feedback in Deep Convolutional Neural Networks for Content Based Image Retrieval”, Hellenic Conference on Artificial Intelligence (SETN), 2016
Regularization in Deep Learning
- M. Tzelepi and A. Tefas, “Improving the performance of lightweight CNNs for binary classification using Quadratic Mutual Information regularization”, Pattern Recognition, 2020
- M. Tzelepi and A. Tefas, “Graph Embedded Convolutional Neural Networks in Human Crowd Detection for Drone Flight Safety”, IEEE Transactions on Emerging Topics in Computational Intelligence, 2019
- M. Tzelepi and A. Tefas, “Class-Specific Discriminant Regularization in realtime deep CNN models for binary classification problems”, Springer Neural Processing Letters, 2019
- M. Tzelepi and A. Tefas, “Improving the Performance of Lightweight CNN Models Using Minimum Enclosing Ball Regularization”, 27th European Signal Processing Conference (EUSIPCO), 2019
Robotics Perception
- M. Tzelepi, C. Symeonidis, N. Nikolaidis and A. Tefas, “Real-time synthetic-to-real human detection for robotics applications”, 13th IEEE International Conference on Information, Intelligence, Systems and Applications (IISA), 2022
- M. Tzelepi, N. Tragkas and A. Tefas, “Improving binary semantic scene segmentation for robotics applications”, 23rd International Conference on Engineering Applications of Neural Networks (EANN), 2022
- M. Tzelepi and A. Tefas, “Semantic Scene Segmentation for Robotics Applications”, 12th International Conference on Information, Intelligence, Systems and Applications (IISA), 2021
- E. Kakaletsis, M. Tzelepi, P. I. Kaplanoglou, C. Symeonidis, N. Nikolaidis, A. Tefas and I. Pitas, “Semantic Map Annotation Through UAV Video Analysis Using Deep Learning Models in ROS”, International Conference on MultiMedia Modeling (MMM), 2019
Timeseries Analysis and Forecasting
- M. Tzelepi, P. Nousi and A. Tefas, “Improving Electric Load Demand Forecasting with Anchor-based Forecasting Method”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023
- A. Andronikos, M. Tzelepi and A. Tefas, “Residual Error Learning for Electricity Demand Forecasting”, International Conference on Engineering Applications of Neural Networks (EANN), 2023
- M. Tzelepi, A. Sapountzaki, N. Maragkos and A. Tefas,” Online Self-Distillation for Electric Load Demand Forecasting on Greek Energy Market”, PAnhellenic Conference on Electronics and Telecommunications (PACET), 2022
- M. Tzelepi and A. Tefas, “Forecasting day-ahead electric load demand on Greek Energy Market”, 13th IEEE International Conference on Information, Intelligence, Systems and Applications (IISA), 2022
- N. Maragkos, M. Tzelepi, N. Passalis, A. Adamakos and A. Tefas, “Electric load demand forecasting on Greek Energy Market using lightweight neural networks”, IEEE Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2022