Adversarially Learned One-Class Classifier for Novelty Detection
CVPR-2018 Novelty detection 是识别在某些方面与训练观察（目标类别）不一样的观察的过程。实际上，Novelty class 在训练期间一般不存在，采样不良或定义不明确。所以，一类分类器能够有效地模拟这些问题。然而，因为来自 Novelty class 的数据不可用，训练端到端深度网络是一项繁琐的任务。在本文中，受到生成对抗网络在无监督和半监督环境中训练深度模型的成功的启发
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相关文章
- 1. 论文速读:Adversarially Learned One-Class Classifier for Novelty Detection [CVPR 2018]
- 2. Adversarially Learned One-Class Classifier for Novelty Detection（用于单类异常检测的对抗学习分类器）
- 3. Kernel PCA for Novelty Detection
- 4. 由Adversarially Learned Inference引发
- 5. A LEARNED REPRESENTATION FOR ARTISTIC STYLE
- 6. Towards Adversarially Robust Object Detection 论文笔记
- 7. Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax(CVPR20)
- 8. 年龄及性别预测（2）AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation
- 9. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 10. Unprocessing Images for Learned Raw Denoising