Description
Inception-ResNet-v2-C is an image model block for an 8 x 8 grid used in the Inception-ResNet-v2 architecture. It largely follows the idea of Inception modules - and grouped convolutions - but also includes residual connections.
Papers Using This Method
Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8, ResNet50, and Inception-ResNet-v2 Deep Learning Models2025-02-13Breccia and basalt classification of thin sections of Apollo rocks with deep learning2024-10-28Enhanced Encoder-Decoder Architecture for Accurate Monocular Depth Estimation2024-10-15Hybrid Inception Architecture with Residual Connection: Fine-tuned Inception-ResNet Deep Learning Model for Lung Inflammation Diagnosis from Chest Radiographs2023-10-04A Transfer Learning Based Approach for Classification of COVID-19 and Pneumonia in CT Scan Imaging2022-10-17Classification of Breast Tumours Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods2022-09-03Danish Fungi 2020 -- Not Just Another Image Recognition Dataset2021-03-18Attention-Driven Body Pose Encoding for Human Activity Recognition2020-09-29An Evaluation of DNN Architectures for Page Segmentation of Historical Newspapers2020-04-15Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets2020-02-142D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy2019-07-30Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations2019-06-01End-to-End Video Captioning2019-04-04Identifying disease-free chest X-ray images with deep transfer learning2019-04-02Deep neural network ensemble by data augmentation and bagging for skin lesion classification2018-07-15Deep Koalarization: Image Colorization using CNNs and Inception-ResNet-v22017-12-09PolyNet: A Pursuit of Structural Diversity in Very Deep Networks2016-11-17Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning2016-02-23