arXiv:1905.03288
Advancements in Image Classification using Convolutional Neural Network
원문 초록
Convolutional Neural Network (CNN) is the state-of-the-art for image classification task. Here we have briefly discussed different components of CNN. In this paper, We have explained different CNN architectures for image classification. Through this paper, we have shown advancements in CNN from LeNet-5 to latest SENet model. We have discussed the model description and training details of each model. We have also drawn a comparison among those models.
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Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification
Hyperspectral Image Classification Method Based on 2D–3D CNN and Multibranch Feature Fusion
Deep Prototypical Networks with Hybrid Residual Attention for Hyperspectral Image Classification
Fast Gradient Descent Algorithm for Image Classification with Neural Networks
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