Deep Learning in Fringe Projection: a Review
H Liu, N Yan, B Shao, S Yuan, X Zhang - Neurocomputing, 2024 - Elsevier
Fringe projection is widely recognized as a prominent technique for 3D measurement, owing
to its non-contact nature, high precision, and exceptional spatial resolution. However, it …
to its non-contact nature, high precision, and exceptional spatial resolution. However, it …
A CNN pruning approach using constrained binary particle swarm optimization with a reduced search space for image classification
Deep convolutional neural networks (CNNs) have exhibited exceptional performance in a
range of computer vision tasks. However, these deep CNNs typically demand significant …
range of computer vision tasks. However, these deep CNNs typically demand significant …
FedGKD: Towards Heterogeneous Federated Learning via Global Knowledge Distillation
Federated learning, as one enabling technology of edge intelligence, has gained substantial
attention due to its efficacy in training deep learning models without data privacy and …
attention due to its efficacy in training deep learning models without data privacy and …
MuxFlow: efficient GPU sharing in production-level clusters with more than 10000 GPUs
Large-scale GPU clusters are widely used to speed up both latency-critical (online) and best-
effort (offline) deep learning (DL) workloads. However, similar to the common practice, the …
effort (offline) deep learning (DL) workloads. However, similar to the common practice, the …
Tsplit: Fine-grained gpu memory management for efficient dnn training via tensor splitting
Since Deep Neural Networks (DNNs) are deeper and larger, performing DNNs training on
existing accelerators (eg, GPUs) is challenging due to their limited device memory capacity …
existing accelerators (eg, GPUs) is challenging due to their limited device memory capacity …
Network intrusion detection based on the temporal convolutional model
Recurrent networks have been adopted as default architecture in approaches performing
sequence modelling of network intrusion detection problems. However, models based on …
sequence modelling of network intrusion detection problems. However, models based on …
The orchestration of Machine Learning frameworks with data streams and GPU acceleration in Kafka‐ML: A deep‐learning performance comparative
Abstract Machine Learning (ML) applications need large volumes of data to train their
models so that they can make high‐quality predictions. Given digital revolution enablers …
models so that they can make high‐quality predictions. Given digital revolution enablers …
Exploring the Impact of Code Clones on Deep Learning Software
R Mo, Y Zhang, Y Wang, S Zhang, P **
software systems across various domains. Deep learning frameworks have been proposed …
software systems across various domains. Deep learning frameworks have been proposed …