Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Disentangled Face Attribute Editing via Instance-Aware Latent Space Search
Authors: Yuxuan Han, Jiaolong Yang, Ying Fu
IJCAI 2021 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |