Gradient Inversion with Generative Image Prior
Authors: Jinwoo Jeon, jaechang Kim, Kangwook Lee, Sewoong Oh, Jungseul Ok
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments Setup. Unless stated otherwise, we consider the image classification task on the validation set of Image Net [22] dataset scaled down to 64 64 pixels (for computational tractability) and use a randomly initialized Res Net18 [10] for training. For deep generative models in GIAS, we use Style GAN2 [13] trained on Image Net. We use a batch size of B = 4 as default and use the negative cosine to measure the gradient dissimilarity d( , ). We present detailed setup in Appendix H. Our experiment code is available at https://github.com/ml-postech/ gradient-inversion-generative-image-prior. |
| Researcher Affiliation | Academia | Jinwoo Jeon1 , Jaechang Kim2 , Kangwook Lee3, Sewoong Oh4, Jungseul Ok1,2 1 Department of Computer Science & Engineering, Pohang University of Science and Technology 2 Graduate School of Artificial Intelligence, Pohang University of Science and Technology 3 Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison 4 Paul G. Allen School of Computer Science & Engineering, University of Washington |
| Pseudocode | Yes | To fully utilize such a pretrained generative model, we propose gradient inversion in alternative spaces (GIAS), of which pseudocode is presented in Appendix A, which performs latent space search over z and then parameter space search over w. |
| Open Source Code | Yes | Our experiment code is available at https://github.com/ml-postech/ gradient-inversion-generative-image-prior. |
| Open Datasets | Yes | Unless stated otherwise, we consider the image classification task on the validation set of Image Net [22] dataset scaled down to 64 64 pixels (for computational tractability) and use a randomly initialized Res Net18 [10] for training. For deep generative models in GIAS, we use Style GAN2 [13] trained on Image Net. For computational tractability, we use DCGAN and images from FFHQ [12] resized to 32x32. |
| Dataset Splits | No | Unless stated otherwise, we consider the image classification task on the validation set of Image Net [22] dataset scaled down to 64 64 pixels (for computational tractability) and use a randomly initialized Res Net18 [10] for training. The paper uses a standard validation set from Image Net but does not explicitly provide the split percentages or absolute sample counts within the text. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions models like ResNet18, StyleGAN2, DCGAN, and optimizers like Adam, but does not specify software library dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We use a batch size of B = 4 as default and use the negative cosine to measure the gradient dissimilarity d( , ). We present detailed setup in Appendix H. |