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pimlphm / Physics Informed Machine Learning Based On TCNA hybrid approach using physical information (PI) lightweight temporal convolutional neural networks (PI-TCN) for remaining useful life (RUL) prediction of bearings under stiffness degradation. It consists of three PI hybrid models: a) PI feature model (PIFM) - constructs physical information health indicators (PIHI) to increase the feature space; b) PI layer model (PILM) - encodes the physics governing equations in a hidden layer; c) PI layer-based loss model (PILLM) - designs PI conflicting losses, taking into account the integration of the physics input-output relationship module into the differences before and after the loss function. I have provided the original model and basic methodology here and welcome further optimisation of the structure and associated training methods. Interestingly, it is not the number of layers of physics knowledge that is more useful; the right structure for the right physics knowledge is the key to success. Similar to pure DL tuning, to design neural networks based on full physical knowledge is a direction that I am very interested in and would like to discuss with you.
Aitical / TCSRIncorporating Transformer Designs into Convolutions for Lightweight Image Super-Resolution
ChipsGuardian / DualConvThis repo is the official implementation of "DualConv: Dual Convolutional Kernels for Lightweight Deep Neural Networks"
DABINB / CSST NetThis study proposes a high-precision multisensor feature fusion fault diagnosis method. The method is based on the lightweight spatial enhancement convolutional module (SConv) with channel shuffling and combined with vision transformer (CSST-Net).
XAIseries / LEXNetLEXNet: A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification
AchunLee / CLOLN TGRSDemo codes for papers "Channel-Layer-Oriented Lightweight Spectral-Spatial Network for Hyperspectral Image Classification" and "Ghostnet for hyperspectral image classification" and "A lightweight spectral-spatial convolution module for hyperspectral image classification"
hokiyoshi / UCAN[CVPR 2026] Unified Convolutional Attention Network for Expansive Receptive Fields in Lightweight Super-Resolution
nasimrahaman / Antipasti TfAntipasti-TF is a lightweight wrapper around Tensorflow for building convolutional neural networks with complex architechtures.
ly27253 / LED NetLED-Net: A lightweight and efficient dual-branch convolutional neural network designed to address the challenge of achieving high-performance tree branch and trunk semantic segmentation in resource-constrained mobile device environments.
ZhouJ6610 / PoseMatch TDCMA lightweight, end-to-end deep learning framework for efficient template matching and in-plane pose estimation, featuring a novel Template-Aware Dynamic Convolution Module (TDCM) and self-supervised geometric alignment. Experiments show our 3.07M model achieves high precision and ∼14 ms inference under compound transformations.
salehinejad / LiteHARLiteHAR: LIGHTWEIGHT HUMAN ACTIVITY RECOGNITION FROM WIFI SIGNALS WITH RANDOM CONVOLUTION KERNELS
GECCOProject / GECCOGECCO is a lightweight image classifier based on single MLP and graph convolutional layers. We find that our model can achieve up to 16x better latency than other state-of-the-art models. The paper for our model can be found at https://arxiv.org/abs/2402.00564
lj107024 / FDDWNetFDDWNET: A LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORK FOR REAL-TIME SEMANTIC SEGMENTATION(ICASSP2020)
dani-capellan / LightTBNetLightTBNet: A lightweight, rapid and efficient deep convolutional network for chest x-ray tuberculosis detection
Salim-Lysiun / Lightweight Convolution TransformerLightweight convolution transformer for cross-patient seizure detection in multi-channel EEG signals
yanzhangnlp / LDGCNsLightweight, Dynamic Graph Convolutional Networksfor AMR-to-Text Generation (EMNLP2020)
liangying-Ke / ILCNNThis repository implements an improved lightweight convolutional neural network (ILCNN) for finger vein recognition.
zhe-meng / LSSCM[GRSL 2022] PyTorch implementation of A lightweight spectral-spatial convolution module for hyperspectral image classification.
abhishekmshr956 / EEGNetEEGNet: A lightweight convolutional neural network for EEG signal classification using PyTorch. This repository includes the model architecture and a training pipeline for efficient EEG signal processing.
JinyuZ1996 / LEA GCNImplementation of "Towards Lightweight Cross-domain Sequential Recommendation via External Attention-enhanced Graph Convolution Network" (DASFAA 2023)
