pcaGAN: Improving Posterior-Sampling cGANs via Principal Component Regularization
Authors: Matthew Bendel, Rizwan Ahmad, Philip Schniter
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments demonstrate that our method outperforms contemporary c GANs and diffusion models in imaging inverse problems like denoising, large-scale inpainting, and accelerated MRI recovery. |
| Researcher Affiliation | Academia | Matthew C. Bendel Dept. ECE The Ohio State University Columbus, OH 43210 EMAIL Rizwan Ahmad Dept. BME The Ohio State University Columbus, OH 43210 EMAIL Philip Schniter Dept. ECE The Ohio State University Columbus, OH 43210 EMAIL |
| Pseudocode | Yes | Algorithm 1 details our proposed approach to training the pca GAN. In particular, it describes the steps used to perform a single update of the generator parameters θ based on the training batch {(xb, yb)}B b=1. Before diving into the details, we offer a brief summary of Algorithm 1. |
| Open Source Code | Yes | The code for our model can be found here: https://github.com/matt-bendel/pca GAN. |
| Open Datasets | Yes | We randomly split the MNIST training fold into 50 000 training and 10 000 validation images, and we use the entire MNIST fold set for testing. |
| Dataset Splits | Yes | For each d, we generate 70 000 training, 20 000 validation, and 10 000 test samples. |
| Hardware Specification | Yes | Running Py Torch on a server with 4 Tesla A100 GPUs, each with 82 GB of memory, the c GAN training for d = 100 takes approximately 8 hours, with training time decreasing with smaller d. |
| Software Dependencies | No | Running Py Torch on a server with 4 Tesla A100 GPUs, each with 82 GB of memory... |
| Experiment Setup | Yes | In each experiment, all c GANs were trained using the Adam optimizer with a learning rate of 10 3, β1 = 0, and β2 = 0.99 as in [55]. We choose βadv = 10 5, nbatch = 64, Prc = 2, and train for 100 epochs for both rc GAN and pca GAN. ... For pca GAN, we choose K = d for each d in this experiment (unless otherwise noted) and βpca = 10 2. |