MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-robust Classifier
Authors: Mozhdeh Rouhsedaghat, Masoud Monajatipoor, C.-C. Jay Kuo, Iacopo Masi
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experimental Evaluation In this section, we investigate MAGIC s capabilities and the effect of the proposed components on synthesized images. We offer an ablation study illustrating the effect of the contributions on our baseline IMAGINE and analyze the improvements. We further compare MAGIC with state-of-the-art by performing qualitative and quantitative evaluations. Quantitative Evaluation. We use machine perception as a proxy for measuring the quality by employing Frechet Inception Distance (FID) by Heusel et al. (2017) and Single Image FID by Shaham, Dekel, and Michaeli (2019). As shown in Tab. 1, MAGIC significantly outperformed DEEPSIM on both object and scene synthesis. To further evaluate our method, we used human perception by conducting subjective evaluation of the image quality for images synthesized by MAGIC compared to DEEPSIM. |
| Researcher Affiliation | Academia | Mozhdeh Rouhsedaghat1, Masoud Monajatipoor2, C.-C. Jay Kuo1, Iacopo Masi3 1 University of Southern California (USC) 2 University of California, Los Angeles (UCLA) 3 Sapienza, University of Rome |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | *Code: https://github.com/mozhdehrouhsedaghat/magic |
| Open Datasets | Yes | Thereby, we replace θ with a quasi-robust model trained on Image Net with Eq. 3 with a ℓ2 perturbation ball centered on the input with a very small ϵ = 0.05. |
| Dataset Splits | Yes | We evaluate MAGIC by conducting extensive experiments on images either randomly selected from the Image Net validation set or collected from the web, or the same images that previous methods used. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory used for running its experiments. |
| Software Dependencies | No | The paper mentions the Adam optimizer and refers to an implementation from another paper, but does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | Yes | For optimizing x , initially the hyper-parameters h in Eq. 4 are set as follows: η = 0.0, γ = 30.0, κ = 1.0, ν = 5.0 while the parameters in ρ(x ) are α = 1e 4 and β = 1e 5. After 5,000 iterations, we start training θd with η = 0.05. This technique improves the alignment of the generated image with y and makes the training process more stable. We use the Adam optimizer with learning rate λ of 5e 4. |