2019/04/19 by Jangho Kim, Minsung Hyun, Kim, Jangho +5 · 33 citations
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Artificial intelligence #Artificial neural network #Classifier (UML) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Data mining #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Feature (linguistics) #Fusion #Machine learning #Pattern recognition (psychology) #cs.CV
paper · pdf · doi:10.48550/arxiv.1904.09058
published in arXiv (Cornell University) (Cornell University) · International Conference on Pattern Recognition
openalex publication_date 2019/04/19 · arxiv created 2020/07/21 · arxiv updated 2020/07/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
We propose a learning framework named Feature Fusion Learning (FFL) that efficiently trains a powerful classifier through a fusion module which combines the feature maps generated from parallel neural networks. Specifically, we train a number of parallel neural networks as sub-networks, then we combine the feature maps from each sub-network using a fusion module to create a more meaningful feature map. The fused feature map is passed into the fused classifier for overall classification. Unlike existing feature fusion methods, in our framework, an ensemble of sub-network classifiers transfers its knowledge to the fused classifier and then the fused classifier delivers its knowledge back to each sub-network, mutually teaching one another in an online-knowledge distillation manner. This mutually teaching system not only improves the performance of the fused classifier but also obtains performance gain in each sub-network. Moreover, our model is more beneficial because different types of network can be used for each sub-network. We have performed a variety of experiments on multiple datasets such as CIFAR-10, CIFAR-100 and ImageNet and proved that our method is more effective than other alternative methods in terms of performance of both sub-networks and the fused classifier.