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..
$\text{ID}^3$: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition
Authors: Jianqing Xu, Shen Li, Jiaying Wu, Miao Xiong, Ailin Deng, Jiazhen Ji, Yuge Huang, Guodong Mu, Wenjie Feng, Shouhong Ding, Bryan Hooi
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across five challenging benchmarks validate the advantages of ID3. Code is released at: https://github.com/hitspring2015/ID3-SFR. |
| Researcher Affiliation | Collaboration | 1Tencent Youtu Lab 2National University of Singapore |
| Pseudocode | Yes | Algorithm 1: Training Algorithm ... Algorithm 2: ID-Preserving Sampling Alg. ... Algorithm 3: Synthetic Dataset Generation |
| Open Source Code | Yes | Code is released at: https://github.com/hitspring2015/ID3-SFR. |
| Open Datasets | Yes | Training Dataset: We train our proposed ID3 on FFHQ (Karras et al., 2019) dataset. ... In order to compare with DCFace (Kim et al., 2023), we also train ID3 on CASIA-Web Face (Yi et al., 2014). |
| Dataset Splits | No | The paper mentions training on FFHQ and CASIA-Web Face and evaluating on several benchmarks, but does not specify explicit train/validation/test splits for the training datasets themselves. |
| Hardware Specification | Yes | All models are implemented with PyTorch and trained from scratch using 8 NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions "implemented with PyTorch" but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For our ID3, we implement the denoising network with a U-net architecture and the projection module with a three-layer perceptron (hidden-layer size (512, 256, 768)) with ReLU activation. ... we set λtκxt = 0.5 (1 1/(1 + exp ( t/T)) for the loss coefficients in Eq. (3), and use T = 1, 000 for the diffusion model; training batch size is set to 16 and the total training steps 500, 000. We directly use a pre-trained face recognition (FR) model sourced from pSp (Richardson et al., 2021) as the identity feature extractor. ... In addition, we set # of identity embeddings m = 25 in Eq. (9) for each ID and match their embeddings with randomly selected attributes as conditioning signals for the diffusion model. For face recognition, we use LResNet50-IR (Deng et al., 2019), a variant of ResNet (He et al., 2016), as the backbone framework and follow the original configurations. |