arXiv:1408.6963
Comment on "Ensemble Projection for Semi-supervised Image Classification"
원문 초록
In a series of papers by Dai and colleagues [1,2], a feature map (or kernel) was introduced for semi- and unsupervised learning. This feature map is build from the output of an ensemble of classifiers trained without using the ground-truth class labels. In this critique, we analyze the latest version of this series of papers, which is called Ensemble Projections [2]. We show that the results reported in [2] were not well conducted, and that Ensemble Projections performs poorly for semi-supervised learning.
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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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