Unsupervised pre-trained filter learning approach for efficient convolution neural network
Abstract The concept of Convolution Neural Network (ConvNet or CNN) is evaluated from
the animal visual cortex. Since humans can learn through experience, similarly, ConvNet …
the animal visual cortex. Since humans can learn through experience, similarly, ConvNet …
Distributed Task Processing Platform for Infrastructure-less IoT Networks: A Multi-dimensional Optimisation Approach
With the rapid development of artificial intelligence (AI) and the Internet of Things (IoT),
intelligent information services have showcased unprecedented capabilities in acquiring …
intelligent information services have showcased unprecedented capabilities in acquiring …
Deep networks for image-to-image translation with mux and demux layers
H Liu, P Navarrete Michelini… - Proceedings of the …, 2018 - openaccess.thecvf.com
Imageprocessingmethodsusingdeepconvol… have achieved great successes on
quantitative and qualitative assessments in many tasks, such as super–resolution, style …
quantitative and qualitative assessments in many tasks, such as super–resolution, style …
[PDF][PDF] Lightweight transfer learning models for ultrasound-guided classification of COVID-19 patients
Lightweight deep convolutional neural networks (CNNs) present a good solution to achieve
fast and accurate image-guided diagnostic procedures of COVID-19 patients. Recently …
fast and accurate image-guided diagnostic procedures of COVID-19 patients. Recently …
Deep learning models for intelligent healthcare: implementation and challenges
The rapid developments of artificial intelligent (AI) is being transformed for its extensive use-
cases, people-centered intelligent systems focusing on care delivery, research encounter …
cases, people-centered intelligent systems focusing on care delivery, research encounter …
DSFEC: Efficient and Deployable Deep Radar Object Detection
G Dandugula, S Boddana, S Mirashi - arxiv preprint arxiv:2412.07411, 2024 - arxiv.org
Deploying radar object detection models on resource-constrained edge devices like the
Raspberry Pi poses significant challenges due to the large size of the model and the limited …
Raspberry Pi poses significant challenges due to the large size of the model and the limited …
SelectQ: Calibration Data Selection for Post-Training Quantization
Post-training quantization (PTQ) can reduce the memory footprint and latency of deep model
inference while still preserving the accuracy of model, with only a small unlabeled …
inference while still preserving the accuracy of model, with only a small unlabeled …
Merging-and-evolution networks for mobile vision applications
Compact neural networks are inclined to exploit “sparsely-connected” convolutions, such as
depthwise convolution and group convolution for employment in mobile applications …
depthwise convolution and group convolution for employment in mobile applications …
Method of transmitting and merging data
YS Lin, WC Chen, TPC Chen - US Patent App. 17/165,096, 2022 - Google Patents
US20220156551A1 - Method of transmitting and merging data - Google Patents
US20220156551A1 - Method of transmitting and merging data - Google Patents Method of …
US20220156551A1 - Method of transmitting and merging data - Google Patents Method of …
Condense-expansion-depth-wise convolutional neural network for face recognition
Y Chen, J Li - US Patent 11,823,033, 2023 - Google Patents
Techniques related to implementing convolutional neural networks for face or other object
recognition are discussed. Such techniques may include applying, in turn, a depth-wise …
recognition are discussed. Such techniques may include applying, in turn, a depth-wise …