Misspecified Phase Retrieval with Generative Priors
Authors: Zhaoqiang Liu, Xinshao Wang, Jiulong Liu
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on image datasets are performed to demonstrate that our approach performs on par with or even significantly outperforms several competing methods. |
| Researcher Affiliation | Academia | Zhaoqiang Liu National University of Singapore dcslizha@nus.edu.sg Xinshao Wang University of Oxford xinshao.wang@eng.ox.ac.uk Jiulong Liu Chinese Academy of Sciences jiulongliu@lsec.cc.ac.cn |
| Pseudocode | Yes | Algorithm 1 A two-step approach for misspecified phase retrieval with generative priors Input: {(ai, yi)}m i=1, step size ζ > 0, number of iterations T1 for the first step, number of iterations T2 for the second step, pre-trained generative model G, initial vector w(0) First step: 1: for t = 0, 1, . . . , T1 1 do 2: w(t+1) = PG Vw(t) 3: end for Second step: Let x(0) := w(T1) 1: for t = 0, 1, . . . , T2 1 do 2: Calculate ˆν(t), y(t) i , x(t+1) and x(t+1) according to (13), (14), (15), and (16), respectively 3: end for Output: ˆx := x(T2) |
| Open Source Code | Yes | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] The code is included in the supplementary material. |
| Open Datasets | Yes | The MNIST dataset contains 60, 000 images of handwritten digits. The size of each image is 28 28, and thus the dimension of the image vector is n = 784. For the MNIST dataset, the generative model G is set to be (the normalized version of) a pre-trained variational autoencoder (VAE) model with the latent dimension being k = 20. We make use of the VAE model pre-trained by the authors of [6] directly. [...] Additional results for the MNIST dataset and some experimental results for the Celeb A [53] dataset are presented in the supplementary material. |
| Dataset Splits | No | The VAE model is trained by the Adam optimizer with a mini-batch size 100 and a learning rate of 0.001, and is trained from the images in the training set. The projection step PG( ) (cf. (11)) is approximated by the Adam optimizer with a learning rate of 0.1 and 120 steps. We report the results on 10 testing images that are selected from the test set. |
| Hardware Specification | Yes | All experiments are run using Python 3.6 and Tensor Flow 1.5.0, with a NVIDIA Ge Force GTX 1080 Ti 11GB GPU. |
| Software Dependencies | Yes | All experiments are run using Python 3.6 and Tensor Flow 1.5.0, with a NVIDIA Ge Force GTX 1080 Ti 11GB GPU. |
| Experiment Setup | Yes | For Algorithm 1, we set T1 = 20 and T2 = 30. As mentioned in Section 3, the starting point w(0) is set to be the column of 1/m sum(yi aiaT i (i.e., a shifted version of V defined in (10)) that corresponds to the largest diagonal entry. In addition, as mentioned in Remark 6, we set the step size ζ as ζ = 1/ˆν(t) (cf. (13)) in the t-th iteration of the second step of Algorithm 1. [...] We follow [37] to set τ = 0.9. For a fair comparison, we use the vector produced after T1 = 20 iterations of the first step of Algorithm 1 as the initialization vector of APPGD, and then we run APPGD for T2 = 30 iterations. |