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Methods/Grouped Convolution

Grouped Convolution

Computer VisionIntroduced 2000575 papers
Source Paper

Description

A Grouped Convolution uses a group of convolutions - multiple kernels per layer - resulting in multiple channel outputs per layer. This leads to wider networks helping a network learn a varied set of low level and high level features. The original motivation of using Grouped Convolutions in AlexNet was to distribute the model over multiple GPUs as an engineering compromise. But later, with models such as ResNeXt, it was shown this module could be used to improve classification accuracy. Specifically by exposing a new dimension through grouped convolutions, cardinality (the size of set of transformations), we can increase accuracy by increasing it.

Papers Using This Method

Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention2025-06-18Analyzing Breast Cancer Survival Disparities by Race and Demographic Location: A Survival Analysis Approach2025-06-08Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare2025-05-22DPN-GAN: Inducing Periodic Activations in Generative Adversarial Networks for High-Fidelity Audio Synthesis2025-05-14Dynamic Pyramid Network for Efficient Multimodal Large Language Model2025-03-26Segmenting Bi-Atrial Structures Using ResNext Based Framework2025-02-28rECGnition_v2.0: Self-Attentive Canonical Fusion of ECG and Patient Data using deep learning for effective Cardiac Diagnostics2025-02-22BILLNET: A Binarized Conv3D-LSTM Network with Logic-gated residual architecture for hardware-efficient video inference2025-01-24Tackling Small Sample Survival Analysis via Transfer Learning: A Study of Colorectal Cancer Prognosis2025-01-21Prediction of Lung Metastasis from Hepatocellular Carcinoma using the SEER Database2025-01-20TakuNet: an Energy-Efficient CNN for Real-Time Inference on Embedded UAV systems in Emergency Response Scenarios2025-01-10Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection2025-01-08Flexible Group Count Enables Hassle-Free Structured Pruning2025-01-01Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXt2024-12-23Thermal Image-based Fault Diagnosis in Induction Machines via Self-Organized Operational Neural Networks2024-12-08Pairwise Discernment of AffectNet Expressions with ArcFace2024-12-01SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented Generation2024-10-15Recognition of Harmful Phytoplankton from Microscopic Images using Deep Learning2024-09-19ALSS-YOLO: An Adaptive Lightweight Channel Split and Shuffling Network for TIR Wildlife Detection in UAV Imagery2024-09-10MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification2024-09-06