Face Recognition is considered to be as one of the finest aspects of Computer Vision, also various Feature Extraction and classification techniques including Neural Network Architectures have made it even more interesting. In this paper, an attempt towards developing a model for better feature ...
Network architecturesThis article presents a comparison of the effectiveness of using different architectures of neural networks in solving the problem of detecting anomalies in time series. The performance characteristics of data storage systems as a subject area are presented. Data is taken from ...
CNNs and RNNs are just two of the most popular categories of neural network architectures. There are dozens of other approaches, and previously obscure types of models are seeing significant growth today. Transformers, like RNNs, are a type of neural network architecture well suited to processing...
We evaluate the pipeline on a real-world medical image dataset and comparatively analyze the performance of four different neural network architectures.DOI: 10.1145/3462462.3468884 年份: 2021 收藏 引用 批量引用 报错 分享 全部来源 求助全文 Semantic Scholar 相似文献...
Based on such word embeddings, several text-processing DNN architectures, like recurrent-neural networks (RNNs) or long-short-term models (LSTMs), have been developed, and many of them have also been adopted for AES tasks (e.g., Alikaniotis et al., 2016; Taghipour & Ng, 2016; Uto &...
D Scherer,A Mu¨Ller,S Behnke - International Conference on Artificial Neural Networks 被引量: 482发表: 2010年 Multisensor Fusion for Computer Vision multisensor fusion for object recognition, network approaches to multisensor fusion, computer architectures for multi sensor fusion, and applications of...
Diverse types of ENN architectures have evolved. These include the simplest ENN, The Naive classifier technique, the generalised ENN and the dynamically weighted ensemble method (DEM) [21]. The latter determines the NN weight at any time the network is estimated and PD faults analysis This ...
After feature embedding and positional embedding, the resulting feature sequence is fed as an input to the Transformer Encoder, which consists of multiple encoder layers, each containing a multi-head self-attention mechanism and a feed-forward neural network. Layer normalization (LN) is applied befor...
In closing, we note that here we have explored the current state of the art in video-frame interpolation. Certainly, one can also expect that our results will further improve as newer and more accurate machine-learning architectures for video-frame interpolation become available. ...
(Kiss and Pirger2013; Maasz et al.2017; Pirger et al.2010a,b,2014,2016), it is highly suitable for such investigations. To accomplish our aim, we first sequenced the whole neural transcriptome ofL. stagnalisand screened it for homologs to the elements of the vertebrate PACAP system. ...