论文标题
培训数据生成网络：通过双层优化形成重建
Training Data Generating Networks: Shape Reconstruction via Bi-level Optimization
论文作者
论文摘要
我们提出了一个新颖的3D形状表示，用于从单个图像中重建3D形状。我们没有直接预测形状，而是训练网络来生成一个训练集，该训练集将被送入另一种学习算法以定义形状。嵌套的优化问题可以通过双层优化建模。具体而言，用于几次学习的元学习方法中也使用了双层优化算法。我们的框架建立了3D形状分析与几乎没有学习之间的联系。我们将培训数据生成网络与双层优化算法相结合，以获得一个完整的框架，可以共同培训所有组件。我们根据3D形状重建的标准基准测试的最新工作改进。
We propose a novel 3d shape representation for 3d shape reconstruction from a single image. Rather than predicting a shape directly, we train a network to generate a training set which will be fed into another learning algorithm to define the shape. The nested optimization problem can be modeled by bi-level optimization. Specifically, the algorithms for bi-level optimization are also being used in meta learning approaches for few-shot learning. Our framework establishes a link between 3D shape analysis and few-shot learning. We combine training data generating networks with bi-level optimization algorithms to obtain a complete framework for which all components can be jointly trained. We improve upon recent work on standard benchmarks for 3d shape reconstruction.
