The segmentation of atrial scan images is of great significance for the three-dimensional reconstruction of the atrium and the surgical positioning.
The high-quality and inner continuity of the images provided by INR has great potential to further improve the spatiotemporal resolution of dynamic MRI, without the need of any training data.
We introduce a method for detecting manipulated videos that is based on the trajectory of the facial region displacement.
We also find that in joint CTC-Attention ASR model, decoder is more sensitive to linguistic information than acoustic information.
Medical image segmentation based on deep learning is often faced with the problems of insufficient datasets and long time-consuming labeling.
In this paper, we present a methodology for fisheries-related data that allows us to converge on a labeled image dataset by iterating over the dataset with multiple training and production loops that can exploit crowdsourcing interfaces.
With the rapid development of generation model, AI-based face manipulation technology, which called DeepFakes, has become more and more realistic.
However, the increased complexity of a model can also introduce high risk of over-fitting, which is a major challenge in SLU tasks due to the limitation of available data.
no code implementations • 10 Oct 2019 • Manzhang Xu, Bijun Tang, Yuhao Lu, Chao Zhu, Lu Zheng, Jingyu Zhang, Nannan Han, Yuxi Guo, Jun Di, Pin Song, Yongmin He, Lixing Kang, Zhiyong Zhang, Wu Zhao, Cuntai Guan, Xuewen Wang, Zheng Liu
Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic device applications but also for the exploration of fundamental physical properties.
A deep learning approach has been proposed recently to derive speaker identifies (d-vector) by a deep neural network (DNN).
We also employ this approach to deal with out-of-language words in the task of multi-lingual speech recognition.
Compared to the conventional layer-wise methods, this new method does not care about the model structure, so can be used to pre-train very complex models.
Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker verification.
Recent research found that a well-trained model can be used as a teacher to train other child models, by using the predictions generated by the teacher model as supervision.