Auxiliary Classifier
Description
Auxiliary Classifiers are type of architectural component that seek to improve the convergence of very deep networks. They are classifier heads we attach to layers before the end of the network. The motivation is to push useful gradients to the lower layers to make them immediately useful and improve the convergence during training by combatting the vanishing gradient problem. They are notably used in the Inception family of convolutional neural networks.
Papers Using This Method
Autoadaptive Medical Segment Anything Model2025-07-02MF2Summ: Multimodal Fusion for Video Summarization with Temporal Alignment2025-06-12A Deep Learning-Driven Inhalation Injury Grading Assistant Using Bronchoscopy Images2025-05-13Semantic segmentation for building houses from wooden cubes2025-03-28DNA Origami Nanostructures Observed in Transmission Electron Microscopy Images can be Characterized through Convolutional Neural Networks2025-03-13RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering2025-02-27A Pragmatic Note on Evaluating Generative Models with Fréchet Inception Distance for Retinal Image Synthesis2025-02-24PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN2025-02-16One-Shot Federated Learning with Classifier-Free Diffusion Models2025-02-12Optimized Unet with Attention Mechanism for Multi-Scale Semantic Segmentation2025-02-06Dual-Flow: Transferable Multi-Target, Instance-Agnostic Attacks via In-the-wild Cascading Flow Optimization2025-02-04A Feature-Level Ensemble Model for COVID-19 Identification in CXR Images using Choquet Integral and Differential Evolution Optimization2025-01-14Parking Space Detection in the City of Granada2025-01-11Multi-classification of High-Frequency Oscillations Using iEEG Signals and Deep Learning Models2024-12-22Annotation-Efficient Task Guidance for Medical Segment Anything2024-12-11Fine-grained Text to Image Synthesis2024-12-10Data Fusion of Semantic and Depth Information in the Context of Object Detection2024-12-04Classification of Geographical Land Structure Using Convolution Neural Network and Transfer Learning2024-11-19A Hybrid Approach for COVID-19 Detection: Combining Wasserstein GAN with Transfer Learning2024-11-10QIANets: Quantum-Integrated Adaptive Networks for Reduced Latency and Improved Inference Times in CNN Models2024-10-14