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Methods/Pointwise Convolution

Pointwise Convolution

Computer VisionIntroduced 20161306 papers

Description

Pointwise Convolution is a type of convolution that uses a 1x1 kernel: a kernel that iterates through every single point. This kernel has a depth of however many channels the input image has. It can be used in conjunction with depthwise convolutions to produce an efficient class of convolutions known as depthwise-separable convolutions.

Image Credit: Chi-Feng Wang

Papers Using This Method

Deploying and Evaluating Multiple Deep Learning Models on Edge Devices for Diabetic Retinopathy Detection2025-06-14EfficientFER: EfficientNetv2 Based Deep Learning Approach for Facial Expression Recognition2025-06-02LD-RPMNet: Near-Sensor Diagnosis for Railway Point Machines2025-06-01OWSM v4: Improving Open Whisper-Style Speech Models via Data Scaling and Cleaning2025-05-31DATD3: Depthwise Attention Twin Delayed Deep Deterministic Policy Gradient For Model Free Reinforcement Learning Under Output Feedback Control2025-05-29Deep Learning-Based Breast Cancer Detection in Mammography: A Multi-Center Validation Study in Thai Population2025-05-29Deep Learning-Based BMD Estimation from Radiographs with Conformal Uncertainty Quantification2025-05-28Intelligent Incident Hypertension Prediction in Obstructive Sleep Apnea2025-05-27Deep Learning for Breast Cancer Detection: Comparative Analysis of ConvNeXT and EfficientNet2025-05-24SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models2025-05-22Comprehensive Lung Disease Detection Using Deep Learning Models and Hybrid Chest X-ray Data with Explainable AI2025-05-21An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI2025-05-21An Exploratory Approach Towards Investigating and Explaining Vision Transformer and Transfer Learning for Brain Disease Detection2025-05-21Vulnerability of Transfer-Learned Neural Networks to Data Reconstruction Attacks in Small-Data Regime2025-05-20Defect Detection in Photolithographic Patterns Using Deep Learning Models Trained on Synthetic Data2025-05-15Real-World fNIRS-Based Brain-Computer Interfaces: Benchmarking Deep Learning and Classical Models in Interactive Gaming2025-05-15Multi-modal wound classification using wound image and location by Xception and Gaussian Mixture Recurrent Neural Network (GMRNN)2025-05-12V-EfficientNets: Vector-Valued Efficiently Scaled Convolutional Neural Network Models2025-05-08Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices2025-05-06PROM: Prioritize Reduction of Multiplications Over Lower Bit-Widths for Efficient CNNs2025-05-06