[HTML][HTML] A review of deep learning-based detection methods for COVID-19
COVID-19 is a fast-spreading pandemic, and early detection is crucial for stopping the
spread of infection. Lung images are used in the detection of coronavirus infection. Chest X …
spread of infection. Lung images are used in the detection of coronavirus infection. Chest X …
Federated learning for computationally constrained heterogeneous devices: A survey
With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …
field, offloading training of neural networks (NNs) to a central server becomes more and …
Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles
Object detection is a significant downstream task in computer vision. For the on-board edge
computing platforms, a giant model is difficult to achieve the real-time detection requirement …
computing platforms, a giant model is difficult to achieve the real-time detection requirement …
Structured knowledge distillation for semantic segmentation
In this paper, we investigate the issue of knowledge distillation for training compact semantic
segmentation networks by making use of cumbersome networks. We start from the …
segmentation networks by making use of cumbersome networks. We start from the …
A CNN-SVM study based on selected deep features for grapevine leaves classification
The main product of grapevines is grapes that are consumed fresh or processed. In addition,
grapevine leaves are harvested once a year as a by-product. The species of grapevine …
grapevine leaves are harvested once a year as a by-product. The species of grapevine …
Late temporal modeling in 3d cnn architectures with bert for action recognition
In this work, we combine 3D convolution with late temporal modeling for action recognition.
For this aim, we replace the conventional Temporal Global Average Pooling (TGAP) layer at …
For this aim, we replace the conventional Temporal Global Average Pooling (TGAP) layer at …
Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization
Modern deep neural networks are typically highly overparameterized. Pruning techniques
are able to remove a significant fraction of network parameters with little loss in accuracy …
are able to remove a significant fraction of network parameters with little loss in accuracy …
Brain-score: Which artificial neural network for object recognition is most brain-like?
The internal representations of early deep artificial neural networks (ANNs) were found to be
remarkably similar to the internal neural representations measured experimentally in the …
remarkably similar to the internal neural representations measured experimentally in the …
Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A …
Abstract Background COVLIAS 1.0: an automated lung segmentation was designed for
COVID-19 diagnosis. It has issues related to storage space and speed. This study shows …
COVID-19 diagnosis. It has issues related to storage space and speed. This study shows …
Sparse: Sparse architecture search for cnns on resource-constrained microcontrollers
The vast majority of processors in the world are actually microcontroller units (MCUs), which
find widespread use performing simple control tasks in applications ranging from …
find widespread use performing simple control tasks in applications ranging from …