Disentangled Face Attribute Editing via Instance-Aware Latent Space Search
Authors: Yuxuan Han, Jiaolong Yang, Ying Fu
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on both GAN-generated and real-world images collectively show that our method outperforms state-of-the-art methods proposed recently by a wide margin. |
| Researcher Affiliation | Collaboration | Yuxuan Han1 , Jiaolong Yang2 , Ying Fu1 1Beijing Institute of Technology 2Mircosoft Research Asia {hanyuxuan, fuying}@bit.edu.cn, jiaoyan@microsoft.com |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/yxuhan/IALS. |
| Open Datasets | Yes | We test our framework on the W space of Style GAN generator trained on the FFHQ [Karras et al., 2018] and Celeb A-HQ [Karras et al., 2019] datasets. The attribute classifiers H( ) are Res Net-18 [He et al., 2016] networks trained on the Celeb A dataset [Liu et al., 2015]. |
| Dataset Splits | No | The paper mentions using FFHQ and CelebA-HQ for training the GAN generator and CelebA for attribute classifiers. It also describes sampling latent codes for evaluation and using DT metrics. However, it does not explicitly provide specific train/validation/test splits with percentages, counts, or references to predefined splits for its own experimental setup. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions using ResNet-18 and ResNet-50 for attribute classifiers but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We empirically set the step size of incremental updating in our method to 0.1 in the following experiments. For each (λ1, λ2) pair, we set nmax = 20 (i.e. sample 20 points on the DT curve) and k = 0.1 and adopt the trapezoidal quadrature formula to approximate the integral in AUC computation defined in Eq. (7). In the following we simply use (λ1, λ2) = (0.75, 0) for our editing method. |