{"url":"/method/global-average-pooling","slug":"global-average-pooling","name":"Global Average Pooling","full_name":"Global Average Pooling","full_name_withheld":false,"description_markdown":"**Global Average Pooling** is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding category of the classification task in the last mlpconv layer. Instead of adding fully connected layers on top of the feature maps, we take the average of each feature map, and the resulting vector is fed directly into the [softmax](https://paperswithcode.com/method/softmax) layer. \r\n\r\nOne advantage of global [average pooling](https://paperswithcode.com/method/average-pooling) over the fully connected layers is that it is more native to the [convolution](https://paperswithcode.com/method/convolution) structure by enforcing correspondences between feature maps and categories. Thus the feature maps can be easily interpreted as categories confidence maps. Another advantage is that there is no parameter to optimize in the global average pooling thus overfitting is avoided at this layer. Furthermore, global average pooling sums out the spatial information, thus it is more robust to spatial translations of the input.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Network In Network","paper":"/paper/network-in-network","first_author":"Min Lin","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/network-in-network"},"source":{"url":"http://arxiv.org/abs/1312.4400v3","title":"Network In Network","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/pytorch/vision/blob/baa592b215804927e28638f6a7f3318cbc411d49/torchvision/models/resnet.py#L157","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Pooling Operations","url":"/methods/category/pooling-operations","pwc_aliases":["pooling-operation"]}],"n_papers_tagged":4076,"archive_num_papers":4076,"papers_newest_first":[{"paper":null,"title":"Automated MRI Tumor Segmentation using hybrid U-Net with Transformer and Efficient Attention","date":"2025-06-18","arxiv_id":"2506.15562","n_code_links":0,"syntology":null},{"paper":null,"title":"Detecting immune cells with label-free two-photon autofluorescence and deep learning","date":"2025-06-17","arxiv_id":"2506.14449","n_code_links":0,"syntology":null},{"paper":null,"title":"Deploying and Evaluating Multiple Deep Learning Models on Edge Devices for Diabetic Retinopathy Detection","date":"2025-06-14","arxiv_id":"2506.14834","n_code_links":0,"syntology":null},{"paper":"/paper/seconnds-secure-outsourced-neural-network","title":"SecONNds: Secure Outsourced Neural Network Inference on ImageNet","date":"2025-06-13","arxiv_id":"2506.11586","n_code_links":2,"syntology":null},{"paper":"/paper/circumventing-backdoor-space-via-weight","title":"Circumventing Backdoor Space via Weight Symmetry","date":"2025-06-09","arxiv_id":"2506.07467","n_code_links":1,"syntology":null},{"paper":null,"title":"Analyzing Breast Cancer Survival Disparities by Race and Demographic Location: A Survival Analysis Approach","date":"2025-06-08","arxiv_id":"2506.07191","n_code_links":0,"syntology":null},{"paper":"/paper/gradual-transition-from-bellman-optimality","title":"Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning","date":"2025-06-06","arxiv_id":"2506.05968","n_code_links":1,"syntology":{"ran":1,"of":4,"unverified":3,"pointer_only":0}},{"paper":null,"title":"Synthetic Speech Source Tracing using Metric Learning","date":"2025-06-03","arxiv_id":"2506.02590","n_code_links":0,"syntology":null},{"paper":null,"title":"PointODE: Lightweight Point Cloud Learning with Neural Ordinary Differential Equations on Edge","date":"2025-05-31","arxiv_id":"2506.00438","n_code_links":0,"syntology":null},{"paper":null,"title":"ACM-UNet: Adaptive Integration of CNNs and Mamba for Efficient Medical Image Segmentation","date":"2025-05-30","arxiv_id":"2505.24481","n_code_links":0,"syntology":null},{"paper":"/paper/optimal-weighted-convolution-for","title":"Optimal Weighted Convolution for Classification and Denosing","date":"2025-05-30","arxiv_id":"2505.24558","n_code_links":2,"syntology":null},{"paper":null,"title":"Stepsize anything: A unified learning rate schedule for budgeted-iteration training","date":"2025-05-30","arxiv_id":"2505.24452","n_code_links":0,"syntology":null},{"paper":null,"title":"Knowledge Distillation for Reservoir-based Classifier: Human Activity Recognition","date":"2025-05-29","arxiv_id":"2505.22985","n_code_links":0,"syntology":null},{"paper":null,"title":"Leveraging Diffusion Models for Synthetic Data Augmentation in Protein Subcellular Localization Classification","date":"2025-05-28","arxiv_id":"2505.22926","n_code_links":0,"syntology":null},{"paper":null,"title":"Intelligent Incident Hypertension Prediction in Obstructive Sleep Apnea","date":"2025-05-27","arxiv_id":"2505.20615","n_code_links":0,"syntology":null},{"paper":null,"title":"Lung Nodule Segmentation: Exploring Data Efficiency and Advanced Architectures","date":"2025-05-26","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"title":"Structured Initialization for Vision Transformers","date":"2025-05-26","arxiv_id":"2505.19985","n_code_links":0,"syntology":null},{"paper":null,"title":"Hierarchical-embedding autoencoder with a predictor (HEAP) as efficient architecture for learning long-term evolution of complex multi-scale physical systems","date":"2025-05-24","arxiv_id":"2505.18857","n_code_links":0,"syntology":null},{"paper":null,"title":"SW-ViT: A Spatio-Temporal Vision Transformer Network with Post Denoiser for Sequential Multi-Push Ultrasound Shear Wave Elastography","date":"2025-05-24","arxiv_id":"2505.18865","n_code_links":0,"syntology":null},{"paper":"/paper/dect-based-space-squeeze-method-for-multi","title":"DECT-based Space-Squeeze Method for Multi-Class Classification of Metastatic Lymph Nodes in Breast Cancer","date":"2025-05-23","arxiv_id":"2505.17528","n_code_links":1,"syntology":null},{"paper":null,"title":"EVM-Fusion: An Explainable Vision Mamba Architecture with Neural Algorithmic Fusion","date":"2025-05-23","arxiv_id":"2505.17367","n_code_links":0,"syntology":null},{"paper":"/paper/the-cell-must-go-on-agar-io-for-continual","title":"The Cell Must Go On: Agar.io for Continual Reinforcement Learning","date":"2025-05-23","arxiv_id":"2505.18347","n_code_links":1,"syntology":null},{"paper":null,"title":"Detailed Evaluation of Modern Machine Learning Approaches for Optic Plastics Sorting","date":"2025-05-22","arxiv_id":"2505.16513","n_code_links":0,"syntology":null},{"paper":null,"title":"Enhancing Federated Survival Analysis through Peer-Driven Client Reputation in Healthcare","date":"2025-05-22","arxiv_id":"2505.16190","n_code_links":0,"syntology":null},{"paper":null,"title":"SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models","date":"2025-05-22","arxiv_id":"2505.16318","n_code_links":0,"syntology":null},{"paper":null,"title":"An Approach Towards Identifying Bangladeshi Leaf Diseases through Transfer Learning and XAI","date":"2025-05-21","arxiv_id":"2505.16033","n_code_links":0,"syntology":null},{"paper":null,"title":"Analysis of ABC Frontend Audio Systems for the NIST-SRE24","date":"2025-05-21","arxiv_id":"2505.15320","n_code_links":0,"syntology":null},{"paper":null,"title":"Comprehensive Lung Disease Detection Using Deep Learning Models and Hybrid Chest X-ray Data with Explainable AI","date":"2025-05-21","arxiv_id":"2505.16028","n_code_links":0,"syntology":null},{"paper":null,"title":"Neural Collapse is Globally Optimal in Deep Regularized ResNets and Transformers","date":"2025-05-21","arxiv_id":"2505.15239","n_code_links":0,"syntology":null},{"paper":null,"title":"Unified Cross-Modal Attention-Mixer Based Structural-Functional Connectomics Fusion for Neuropsychiatric Disorder Diagnosis","date":"2025-05-21","arxiv_id":"2505.15139","n_code_links":0,"syntology":null}],"papers_shown":30,"tasks":[{"task":"/task/image-classification","name":"Image Classification","papers":573},{"task":"/task/object-detection","name":"Object Detection","papers":465},{"task":"/task/image-classification","name":"image-classification","papers":437},{"task":"/task/object-detection-1","name":"object-detection","papers":414},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":317},{"task":"/task/classification","name":"General Classification","papers":278},{"task":"/task/transfer-learning","name":"Transfer Learning","papers":274},{"task":"/task/classification-1","name":"Classification","papers":265},{"task":"/task/object","name":"Object","papers":208},{"task":"/task/segmentation","name":"Segmentation","papers":207},{"task":"/task/data-augmentation","name":"Data Augmentation","papers":182},{"task":"/task/self-supervised-learning","name":"Self-Supervised Learning","papers":170},{"task":"/task/representation-learning","name":"Representation Learning","papers":160},{"task":"/task/contrastive-learning","name":"Contrastive Learning","papers":159},{"task":"/task/deep-learning","name":"Deep Learning","papers":159},{"task":null,"name":"GPU","papers":125},{"task":"/task/reinforcement-learning-1","name":"Reinforcement Learning (RL)","papers":108},{"task":"/task/architecture-search","name":"Neural Architecture Search","papers":106},{"task":"/task/quantization","name":"Quantization","papers":100},{"task":"/task/instance-segmentation","name":"Instance Segmentation","papers":97}],"tasks_shown":20,"n_tasks":1096,"usage_by_year":[{"year":"2013","papers":1},{"year":"2014","papers":1},{"year":"2015","papers":4},{"year":"2016","papers":43},{"year":"2017","papers":136},{"year":"2018","papers":286},{"year":"2019","papers":464},{"year":"2020","papers":625},{"year":"2021","papers":694},{"year":"2022","papers":551},{"year":"2023","papers":529},{"year":"2024","papers":550},{"year":"2025","papers":192}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/global-average-pooling"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}