Learning FRAME Models Using CNN Filters for Knowledge Visualization
使用CNN过滤器学习FRAME模型以进行知识可视化 卷积神经网络（ConvNet或CNN）已被证明在许多任务中非常成功 就像计算机视觉领域的人一样 在此概念文件中，我们研究了可分辨CNN的生成视角。尤其是，我们建议使用CNN在卷积层预先学习的高表达滤波器来学习生成FRAME（滤波器，随机场和最大熵）模型。 我们证明了该学习算法可以在自然场景中生成逼真的丰富对象和纹理图案。我们解释说，每个学习的模型
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相关文章
- 1. Learning Spatially Regularized Correlation Filters for Visual Tracking （SRDCF）
- 2. SemEval2019Task3_ERC | (3) Using Deep Sentiment Analysis Models and Transfer Learning for ERC
- 3. StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performa
- 4. Knowledge Distillation论文阅读（2）：Learning Efficient Object Detection Models with Knowledge Distillation
- 5. visualization of filters keras 基于Keras的卷积神经网络（CNN）可视化
- 6. CNN for NLP
- 7. 论文阅读 | Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models i
- 8. Research Guide for Video Frame Interpolation with Deep Learning
- 9. Visualization for Airbnb Paris Data
- 10. Rethinking Knowledge Graph Propagation for Zero-Shot Learning