擴展球形卷積神經網絡

摘要

球形 CNN 將 CNN 推廣至球面上的函數，主要使用球形卷積作為線性運算。計算球形卷積最準確且高效的方式是在頻譜域中（透過卷積定理），但仍比通常的平面卷積更昂貴。因此，迄今為止，球形 CNN 的應用僅限於可以以較低模型容量解決的小問題。在這項研究中，我們展示了如何將球形 CNN 擴展至更大的問題。為了實現這一目標，我們進行了關鍵改進，包括常見模型組件的新變體、實現核心操作以利用硬體加速器特性，以及利用我們模型特性的特定應用輸入表示。實驗表明，我們更大的球形 CNN 在 QM9 分子基準的多個目標上達到了最先進水準，該基準以前主要由等變圖神經網絡主導，並在多個天氣預報任務上取得了競爭性表現。我們的程式碼可在 https://github.com/google-research/spherical-cnn 找到。

English

Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation. The most accurate and efficient way to compute spherical convolutions is in the spectral domain (via the convolution theorem), which is still costlier than the usual planar convolutions. For this reason, applications of spherical CNNs have so far been limited to small problems that can be approached with low model capacity. In this work, we show how spherical CNNs can be scaled for much larger problems. To achieve this, we make critical improvements including novel variants of common model components, an implementation of core operations to exploit hardware accelerator characteristics, and application-specific input representations that exploit the properties of our model. Experiments show our larger spherical CNNs reach state-of-the-art on several targets of the QM9 molecular benchmark, which was previously dominated by equivariant graph neural networks, and achieve competitive performance on multiple weather forecasting tasks. Our code is available at https://github.com/google-research/spherical-cnn.

擴展球形卷積神經網絡

Scaling Spherical CNNs

摘要

Support