Just Least Squares: Binary Compressive Sampling with Low Generative Intrinsic Dimension
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In this paper, we consider recovering n dimensional signals from m binary measurements corrupted by noises and sign flips under the assumption that the target signals have low generative intrinsic dimension, i.e., the target signals can be approximately generated via an L-Lipschitz generator G: ℝ^k→ℝ^n, k≪ n. Although the binary measurements model is highly nonlinear, we propose a least square decoder and prove that, up to a constant c, with high probability, the least square decoder achieves a sharp estimation error 𝒪 (√(klog (Ln)/m)) as long as m≥𝒪( klog (Ln)). Extensive numerical simulations and comparisons with state-of-the-art methods demonstrated the least square decoder is robust to noise and sign flips, as indicated by our theory. By constructing a ReLU network with properly chosen depth and width, we verify the (approximately) deep generative prior, which is of independent interest.
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