{"url":"/method/inception-c","slug":"inception-c","name":"Inception-C","full_name":"Inception-C","full_name_withheld":false,"description_markdown":"**Inception-C** is an image model block used in the [Inception-v4](https://paperswithcode.com/method/inception-v4) architecture.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning","paper":"/paper/inception-v4-inception-resnet-and-the-impact","first_author":"Christian Szegedy","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/inception-v4-inception-resnet-and-the-impact"},"source":{"url":"http://arxiv.org/abs/1602.07261v2","title":"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/kentsommer/keras-inceptionV4/blob/ef1db6f09b6511779c05fab47d374741bc89b5ee/inception_v4.py#L156","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Image Model Blocks","url":"/methods/category/image-model-blocks","pwc_aliases":[]}],"n_papers_tagged":13,"archive_num_papers":13,"papers_newest_first":[{"paper":null,"title":"Multi-Label Scene Classification in Remote Sensing Benefits from Image Super-Resolution","date":"2025-01-12","arxiv_id":"2501.06720","n_code_links":0,"syntology":null},{"paper":null,"title":"DeepGI: An Automated Approach for Gastrointestinal Tract Segmentation in MRI Scans","date":"2024-01-27","arxiv_id":"2401.15354","n_code_links":0,"syntology":null},{"paper":"/paper/semi-supervised-vision-transformers-at-scale","title":"Semi-supervised Vision Transformers at Scale","date":"2022-08-11","arxiv_id":"2208.05688","n_code_links":1,"syntology":null},{"paper":null,"title":"Ensemble CNN models for Covid-19 Recognition and Severity Perdition From 3D CT-scan","date":"2022-06-29","arxiv_id":"2206.15431","n_code_links":0,"syntology":null},{"paper":"/paper/danish-fungi-2020-not-just-another-image","title":"Danish Fungi 2020 -- Not Just Another Image Recognition Dataset","date":"2021-03-18","arxiv_id":"2103.10107","n_code_links":1,"syntology":null},{"paper":"/paper/comprehensive-comparison-of-deep-learning","title":"Comprehensive Comparison of Deep Learning Models for Lung and COVID-19 Lesion Segmentation in CT scans","date":"2020-09-10","arxiv_id":"2009.06412","n_code_links":1,"syntology":null},{"paper":"/paper/effects-of-approximate-multiplication-on","title":"The Effects of Approximate Multiplication on Convolutional Neural Networks","date":"2020-07-20","arxiv_id":"2007.10500","n_code_links":1,"syntology":null},{"paper":null,"title":"A Deep Learning based Wearable Healthcare IoT Device for AI-enabled Hearing Assistance Automation","date":"2020-05-16","arxiv_id":"2005.08076","n_code_links":0,"syntology":null},{"paper":null,"title":"Effective, Fast, and Memory-Efficient Compressed Multi-function Convolutional Neural Networks for More Accurate Medical Image Classification","date":"2018-11-29","arxiv_id":"1811.11996","n_code_links":0,"syntology":null},{"paper":null,"title":"Deep neural network ensemble by data augmentation and bagging for skin lesion classification","date":"2018-07-15","arxiv_id":"1807.05496","n_code_links":0,"syntology":null},{"paper":null,"title":"Improved Inception-Residual Convolutional Neural Network for Object Recognition","date":"2017-12-28","arxiv_id":"1712.09888","n_code_links":0,"syntology":null},{"paper":"/paper/recod-titans-at-isic-challenge-2017","title":"RECOD Titans at ISIC Challenge 2017","date":"2017-03-14","arxiv_id":"1703.04819","n_code_links":4,"syntology":null},{"paper":"/paper/inception-v4-inception-resnet-and-the-impact","title":"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning","date":"2016-02-23","arxiv_id":"1602.07261","n_code_links":87,"syntology":{"ran":1,"of":1,"unverified":0,"pointer_only":0}}],"papers_shown":13,"tasks":[{"task":"/task/classification","name":"General Classification","papers":5},{"task":"/task/classification-1","name":"Classification","papers":3},{"task":"/task/image-classification","name":"Image Classification","papers":3},{"task":"/task/image-segmentation","name":"Image Segmentation","papers":2},{"task":"/task/lesion-segmentation","name":"Lesion Segmentation","papers":2},{"task":"/task/meta-learning","name":"Meta-Learning","papers":2},{"task":"/task/segmentation","name":"Segmentation","papers":2},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":2},{"task":"/task/classifier-calibration","name":"Classifier calibration","papers":1},{"task":"/task/data-augmentation","name":"Data Augmentation","papers":1},{"task":"/task/fine-grained-image-classification","name":"Fine-Grained Image Classification","papers":1},{"task":"/task/fine-grained-image-recognition","name":"Fine-Grained Image Recognition","papers":1},{"task":null,"name":"GPU","papers":1},{"task":"/task/image-super-resolution","name":"Image Super-Resolution","papers":1},{"task":"/task/inductive-bias","name":"Inductive Bias","papers":1},{"task":"/task/lesion-classification","name":"Lesion Classification","papers":1},{"task":"/task/medical-image-classification","name":"Medical Image Classification","papers":1},{"task":"/task/medical-image-segmentation","name":"Medical Image Segmentation","papers":1},{"task":"/task/object","name":"Object","papers":1},{"task":"/task/object-recognition","name":"Object Recognition","papers":1}],"tasks_shown":20,"n_tasks":29,"usage_by_year":[{"year":"2016","papers":1},{"year":"2017","papers":2},{"year":"2018","papers":2},{"year":"2020","papers":3},{"year":"2021","papers":1},{"year":"2022","papers":2},{"year":"2024","papers":1},{"year":"2025","papers":1}],"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/inception-c"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}