Fine-tuning visual models has been widely shown promising performance on many downstream visual tasks. With the surprising development of pre-trained visual foundation …
Spiking neural networks (SNNs) that mimic information transmission in the brain can energy- efficiently process spatio-temporal information through discrete and sparse spikes, thereby …
Transformer-based Deep Neural Network architectures have gained tremendous interest due to their effectiveness in various applications across Natural Language Processing (NLP) …
BM Oloulade, J Gao, J Chen, T Lyu… - Tsinghua Science and …, 2021 - ieeexplore.ieee.org
In academia and industries, graph neural networks (GNNs) have emerged as a powerful approach to graph data processing ranging from node classification and link prediction tasks …
W Ye, T Yan, C Zhang, L Duan, W Chen, H Song… - Foods, 2022 - mdpi.com
Rapid and accurate detection of pesticide residue levels can help to prevent the harm of pesticide residue. This study used visible/near-infrared (Vis-NIR)(376–1044 nm) and near …
X Li, J Zheng, M Li, W Ma, Y Hu - Expert Systems with Applications, 2022 - Elsevier
Abstract Machine learning method has been widely applied in industrial fault diagnosis, especially the deep learning method. In the field of industrial fault diagnosis, deep learning …
Neural Architecture Search (NAS), a promising and fast-moving research field, aims to automate the architectural design of Deep Neural Networks (DNNs) to achieve better …
X Luo, D Liu, H Kong, S Huai… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Neural architecture search (NAS) is an emerging paradigm to automate the design of competitive deep neural networks (DNNs). In practice, DNNs are subject to strict latency …
Deep neural networks (DNNs) based methods have achieved great success in single image super-resolution (SISR). However, existing state-of-the-art SISR techniques are designed …