TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Methods/Depthwise Convolution

Depthwise Convolution

Computer VisionIntroduced 20161321 papers

Description

Depthwise Convolution is a type of convolution where we apply a single convolutional filter for each input channel. In the regular 2D convolution performed over multiple input channels, the filter is as deep as the input and lets us freely mix channels to generate each element in the output. In contrast, depthwise convolutions keep each channel separate. To summarize the steps, we:

  1. Split the input and filter into channels.
  2. We convolve each input with the respective filter.
  3. We stack the convolved outputs together.

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 Machines2025-06-01DATD3: 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 Regime2025-05-20SAFEPATH: Preventing Harmful Reasoning in Chain-of-Thought via Early Alignment2025-05-20WriteViT: Handwritten Text Generation with Vision Transformer2025-05-19Defect 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 Devices2025-05-06