Zobrazeno 1 - 10
of 4 150
pro vyhledávání: '"Synthetic training data"'
Computational text classification is a challenging task, especially for multi-dimensional social constructs. Recently, there has been increasing discussion that synthetic training data could enhance classification by offering examples of how these co
Externí odkaz:
http://arxiv.org/abs/2410.12622
Relation extraction is crucial for constructing knowledge graphs, with large high-quality datasets serving as the foundation for training, fine-tuning, and evaluating models. Generative data augmentation (GDA) is a common approach to expand such data
Externí odkaz:
http://arxiv.org/abs/2410.08393
The use of synthetic speech as data augmentation is gaining increasing popularity in fields such as automatic speech recognition and speech classification tasks. Despite novel text-to-speech systems with voice cloning capabilities, that allow the usa
Externí odkaz:
http://arxiv.org/abs/2409.12745
Large language models (LLMs) have shown great potential for automatic code generation and form the basis for various tools such as GitHub Copilot. However, recent studies highlight that many LLM-generated code contains serious security vulnerabilitie
Externí odkaz:
http://arxiv.org/abs/2409.06446
In this work we evaluate the utility of synthetic data for training automatic speech recognition (ASR). We use the ASR training data to train a text-to-speech (TTS) system similar to FastSpeech-2. With this TTS we reproduce the original training data
Externí odkaz:
http://arxiv.org/abs/2407.17997
Autor:
Chollet, Etienne, Balbastre, Yaël, Mauri, Chiara, Magnain, Caroline, Fischl, Bruce, Wang, Hui
Microvascular anatomy is known to be involved in various neurological disorders. However, understanding these disorders is hindered by the lack of imaging modalities capable of capturing the comprehensive three-dimensional vascular network structure
Externí odkaz:
http://arxiv.org/abs/2407.01419
Publikováno v:
Proceedings of the 27th Computer Vision Winter Workshop CVWW (2024) 29-37
The goal of this paper is to assess the impact of noise in 3D camera-captured data by modeling the noise of the imaging process and applying it on synthetic training data. We compiled a dataset of specifically constructed scenes to obtain a noise mod
Externí odkaz:
http://arxiv.org/abs/2402.16514
In industrial manufacturing, numerous tasks of visually inspecting or detecting specific objects exist that are currently performed manually or by classical image processing methods. Therefore, introducing recent deep learning models to industrial en
Externí odkaz:
http://arxiv.org/abs/2403.04809
Autor:
Lin, Zhi-Yi, Lyu, Bofan, Fernandez, Judith Cueto, van der Kruk, Eline, Seth, Ajay, Zhang, Xucong
Accurate 3D kinematics estimation of human body is crucial in various applications for human health and mobility, such as rehabilitation, injury prevention, and diagnosis, as it helps to understand the biomechanical loading experienced during movemen
Externí odkaz:
http://arxiv.org/abs/2402.13172
Autor:
Fang, Meiling, Huber, Marco, Fierrez, Julian, Ramachandra, Raghavendra, Damer, Naser, Alkhaddour, Alhasan, Kasantcev, Maksim, Pryadchenko, Vasiliy, Yang, Ziyuan, Huangfu, Huijie, Chen, Yingyu, Zhang, Yi, Pan, Yuchen, Jiang, Junjun, Liu, Xianming, Sun, Xianyun, Wang, Caiyong, Liu, Xingyu, Chang, Zhaohua, Zhao, Guangzhe, Tapia, Juan, Gonzalez-Soler, Lazaro, Aravena, Carlos, Schulz, Daniel
This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition attract
Externí odkaz:
http://arxiv.org/abs/2311.05336