Zobrazeno 1 - 10
of 2 432
pro vyhledávání: '"Pedró, C."'
Leveraging the capabilities of Knowledge Distillation (KD) strategies, we devise a strategy to fight the recent retraction of face recognition datasets. Given a pretrained Teacher model trained on a real dataset, we show that carefully utilising synt
Externí odkaz:
http://arxiv.org/abs/2408.17399
As in school, one teacher to cover all subjects is insufficient to distill equally robust information to a student. Hence, each subject is taught by a highly specialised teacher. Following a similar philosophy, we propose a multiple specialized teach
Externí odkaz:
http://arxiv.org/abs/2408.16563
This work provides a complete rheological characterization of molybdenum disulfide (MoS2) inks in the presence of electric fields. Several concentrations of MoS2 are studied and dispersed in a viscoelastic fluid. The lubrication effects are present i
Externí odkaz:
http://arxiv.org/abs/2408.11506
This study investigates the effects of occlusions on the fairness of face recognition systems, particularly focusing on demographic biases. Using the Racial Faces in the Wild (RFW) dataset and synthetically added realistic occlusions, we evaluate the
Externí odkaz:
http://arxiv.org/abs/2408.10175
Autor:
Drinko, Alexandre, Correr, Guilherme I., Medina, Ivan, Azado, Pedro C., Canabarro, Askery, Soares-Pinto, Diogo O.
Variational quantum algorithms (VQAs) have emerged in recent years as a promise to obtain quantum advantage. These task-oriented algorithms work in a hybrid loop combining a quantum processor and classical optimization. Using a specific class of VQA
Externí odkaz:
http://arxiv.org/abs/2407.04453
Parameterized quantum circuits play a key role for the development of quantum variational algorithms in the realm of the NISQ era. Knowing their actual capability of performing different kinds of tasks is then of the utmost importance. By comparing t
Externí odkaz:
http://arxiv.org/abs/2405.19537
Autor:
Correr, Guilherme Ilário, Medina, Ivan, Azado, Pedro C., Drinko, Alexandre, Soares-Pinto, Diogo O.
While scalable error correction schemes and fault tolerant quantum computing seem not to be universally accessible in the near sight, the efforts of many researchers have been directed to the exploration of the contemporary available quantum hardware
Externí odkaz:
http://arxiv.org/abs/2405.02265
Face recognition applications have grown in parallel with the size of datasets, complexity of deep learning models and computational power. However, while deep learning models evolve to become more capable and computational power keeps increasing, th
Externí odkaz:
http://arxiv.org/abs/2404.15234
Autor:
DeAndres-Tame, Ivan, Tolosana, Ruben, Melzi, Pietro, Vera-Rodriguez, Ruben, Kim, Minchul, Rathgeb, Christian, Liu, Xiaoming, Morales, Aythami, Fierrez, Julian, Ortega-Garcia, Javier, Zhong, Zhizhou, Huang, Yuge, Mi, Yuxi, Ding, Shouhong, Zhou, Shuigeng, He, Shuai, Fu, Lingzhi, Cong, Heng, Zhang, Rongyu, Xiao, Zhihong, Smirnov, Evgeny, Pimenov, Anton, Grigorev, Aleksei, Timoshenko, Denis, Asfaw, Kaleb Mesfin, Low, Cheng Yaw, Liu, Hao, Wang, Chuyi, Zuo, Qing, He, Zhixiang, Shahreza, Hatef Otroshi, George, Anjith, Unnervik, Alexander, Rahimi, Parsa, Marcel, Sébastien, Neto, Pedro C., Huber, Marco, Kolf, Jan Niklas, Damer, Naser, Boutros, Fadi, Cardoso, Jaime S., Sequeira, Ana F., Atzori, Andrea, Fenu, Gianni, Marras, Mirko, Štruc, Vitomir, Yu, Jiang, Li, Zhangjie, Li, Jichun, Zhao, Weisong, Lei, Zhen, Zhu, Xiangyu, Zhang, Xiao-Yu, Biesseck, Bernardo, Vidal, Pedro, Coelho, Luiz, Granada, Roger, Menotti, David
Publikováno v:
IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRw 2024)
Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some c
Externí odkaz:
http://arxiv.org/abs/2404.10378
Publikováno v:
Advances in Intelligent Data Analysis XXII. IDA 2024. Lecture Notes in Computer Science, vol 14642., pp 95-106 (2024). Springer, Cham
We present S+t-SNE, an adaptation of the t-SNE algorithm designed to handle infinite data streams. The core idea behind S+t-SNE is to update the t-SNE embedding incrementally as new data arrives, ensuring scalability and adaptability to handle stream
Externí odkaz:
http://arxiv.org/abs/2403.17643