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We propose and demonstrate a generative deep learning approach for the shaperecognition of an arbitrary object from its acoustic scattering properties. Thestrategy exploits deep neural networks to learn the mapping between the latentspace of a two-dimensional acoustic object and the far-field scatteringamplitudes. A neural network is designed as an Adversarial autoencoder andtrained via unsupervised learning to determine the latent space of the acousticobject. Important structural features of the object are embedded inlower-dimensional latent space which supports the modeling of a shape generatorand accelerates the learning in the inverse design process.The proposed inversedesign uses the variational inference approach with encoder and decoder-likearchitecture where the decoder is composed of two pretrained neural networks,the generator and the forward model. The data-driven framework finds anaccurate solution to the ill-posed inverse scattering problem, where non-uniquesolution space is overcome by the multifrequency phaseless far-field patterns.This inverse method is a powerful design tool that does not require complexanalytical calculation and opens up new avenues for practical realization,automatic recognition of arbitrary shaped submarines or large fish, and otherunderwater applications.

A Generative deep learning approach for shape recognition of arbitrary objects from phaseless acoustic scattering data