arXiv:1810.03052
공식 코드 · Papers with Code
Deep convolutional Gaussian processes
원문 초록
We propose deep convolutional Gaussian processes, a deep Gaussian process architecture with convolutional structure. The model is a principled Bayesian framework for detecting hierarchical combinations of local features for image classification. We demonstrate greatly improved image classification performance compared to current Gaussian process approaches on the MNIST and CIFAR-10 datasets. In particular, we improve CIFAR-10 accuracy by over 10 percentage points.
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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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