Description
Inception v2 is the second generation of Inception convolutional neural network architectures which notably uses batch normalization. Other changes include dropping dropout and removing local response normalization, due to the benefits of batch normalization.
Papers Using This Method
Data Fusion of Semantic and Depth Information in the Context of Object Detection2024-12-04Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch Retrieval2023-08-13Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse2021-09-02An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic2021-01-05Lane Change For System-Driven Vehicles Using Dynamic Information2020-10-01Perceptual Modelling of Unconstrained Road Traffic Scenarios with Deep Learning2020-09-30Perceptual Modelling of Unconstrained Road Traffic Scenarios with Deep Learning2020-09-30Synthesizing Unrestricted False Positive Adversarial Objects Using Generative Models2020-05-19On the safety of vulnerable road users by cyclist orientation detection using Deep Learning2020-04-25Identifying Individual Dogs in Social Media Images2020-03-14Traffic Signs Detection and Recognition System using Deep Learning2020-03-06Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets2020-02-14AI-based Pilgrim Detection using Convolutional Neural Networks2019-11-18Inception-inspired LSTM for Next-frame Video Prediction2019-08-28Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift2015-02-11