论文标题
革兰氏正则化多视图3D形状检索
Gram Regularization for Multi-view 3D Shape Retrieval
论文作者
论文摘要
如何获得3D形状的理想表示是3D形状检索任务中的关键挑战。大多数现有的3D形状检索方法着重于使用不同的神经网络体系结构捕获形状表示，而网络中每一层的学习能力被忽略了。限制网络能力的一个常见和棘手的问题是过度拟合。为了解决这个问题，L2正则化广泛应用于现有的深度学习框架。但是，使用L2正则化对概括能力的影响受到限制，因为它仅控制参数的较大值。为了弥补差距，在本文中，我们提出了一个称为革兰氏正则化的新颖正则化术语，该术语通过鼓励重量内核在相应的特征图上提取不同的信息来增强网络的学习能力。通过迫使重量内核之间的差异很大，常规器可以帮助提取区分特征。所提出的革兰氏正则化是独立的，并且可以稳定而迅速地收敛，而无需铃铛和哨声。此外，它可以很容易地插入现有的现成架构中。对流行的3D对象检索基准模型网的广泛实验结果证明了我们方法的有效性。
How to obtain the desirable representation of a 3D shape is a key challenge in 3D shape retrieval task. Most existing 3D shape retrieval methods focus on capturing shape representation with different neural network architectures, while the learning ability of each layer in the network is neglected. A common and tough issue that limits the capacity of the network is overfitting. To tackle this, L2 regularization is applied widely in existing deep learning frameworks. However,the effect on the generalization ability with L2 regularization is limited as it only controls large value in parameters. To make up the gap, in this paper, we propose a novel regularization term called Gram regularization which reinforces the learning ability of the network by encouraging the weight kernels to extract different information on the corresponding feature map. By forcing the variance between weight kernels to be large, the regularizer can help to extract discriminative features. The proposed Gram regularization is data independent and can converge stably and quickly without bells and whistles. Moreover, it can be easily plugged into existing off-the-shelf architectures. Extensive experimental results on the popular 3D object retrieval benchmark ModelNet demonstrate the effectiveness of our method.
