Abstract: We study the robust one-bit compressed sensing problem whose goal is to design an algorithm that faithfully recovers any sparse target vector ${\theta }_0\in {\mathbb{R}}^d$ \emph{uniformly} from $m$ quantized noisy measurements. Under the assumption that the measurements are sub-Gaussian, to recover any $k$-sparse ${\theta }_0$ ($k\ll d$) \emph{uniformly} up to an error $\epsilon$ with high probability, the best known computationally tractable algorithm requires\footnote{Here, an algorithm is ``computationally tractable'' if it has provable convergence guarantees. The notation $\tilde{\mathcal{O}}(\cdot )$ omits a logarithm factor of ${\epsilon }^{-1}$.} $m\ge \tilde{\mathcal{O}}(k\log d/{\epsilon }^4)$. In this paper, we consider a new framework for the one-bit sensing problem where the sparsity is implicitly enforced via mapping a low dimensional representation $x_0$ through a known $n$-layer ReLU generative network $G:{\mathbb{R}}^k\to {\mathbb{R}}^d$. Such a framework poses low-dimensional priors on ${\theta }_0$ without a known basis. We propose to recover the target $G(x_0)$ via an unconstrained empirical risk minimization (ERM) problem under a much weaker \emph{sub-exponential measurement assumption}. For such a problem, we establish a joint statistical and computational analysis. In particular, we prove that the ERM estimator in this new framework achieves an improved statistical rate of $m=\tilde{\mathcal{O}}(kn\log d/{ϵ}^2)$ recovering any $G(x_0)$ uniformly up to an error $\epsilon$. Moreover, from the lens of computation, despite non-convexity, we prove that the objective of our ERM problem has no spurious stationary point, that is, any stationary point is equally good for recovering the true target up to scaling with a certain accuracy. Our analysis sheds some light on the possibility of inverting a deep generative model under partial and quantized measurements, complementing the recent success of using deep generative models for inverse problems.