We propose a Multiscale Invertible Generative Network (MsIGN) and associated training algorithm that leverages multiscale structure to solve high-dimensional Bayesian inference. To address the curse of dimensionality, MsIGN exploits the low-dimensional nature of the posterior, and generates samples from coarse to fine scale (low to high dimension) by iteratively upsampling and refining samples. MsIGN is trained in a multi-stage manner to minimize the Jeffreys divergence, which avoids mode dropping in high-dimensional cases. On two high-dimensional Bayesian inverse problems, we show superior performance of MsIGN over previous approaches in posterior approximation and multiple mode capture. On the natural image synthesis task, MsIGN achieves superior performance in bits-per-dimension over baseline models and yields great interpret-ability of its neurons in intermediate layers.
翻译:我们建议建立一个多尺度、可垂直生成网络和相关的培训算法,利用多尺度结构解决高维贝叶斯推论。为了解决维度的诅咒,Msign利用了后方的低维性质,通过迭代式采样和精炼样品,从粗到精细的样本(低至高维)生成。Msign以多阶段方式接受培训,以尽量减少杰弗里斯差异,避免高维情况下的模式下降。关于两个高维贝伊斯反向问题,我们展示了Msign优于先前的近似和多模式捕捉方法的优异性。关于自然图像合成任务,Msign在比基底模型的比特差上取得优异性,并在中间层产生神经的可解释性。