Demodalizing Face Recognition with Synthetic Samples
Authors: Zhonghua Zhai, Pengju Yang, Xiaofeng Zhang, Maji Huang, Haijing Cheng, Xuejun Yan, Chunmao Wang, Shiliang Pu3278-3286
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the effectiveness of our approach on various benchmarks of large-scale face recognition and outperform the previous methods, especially in the low FAR range. |
| Researcher Affiliation | Collaboration | 1Hikvision Research Institute 2Zhejiang University |
| Pseudocode | No | The paper describes the proposed methods using textual descriptions and figures but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions a GitHub link for a dataset built (CP-IJB-C) but does not explicitly state that the source code for the proposed DMFR methodology is open source or provide a link for it. |
| Open Datasets | Yes | We use a cleaned version of the MSCeleb-1M datasets (Guo et al. 2016) with 2,251,420 images of 58,982 subjects as our training set. |
| Dataset Splits | No | The paper mentions using a training set (MSCeleb-1M) and various test sets (LFW, YTF, IJB-A, etc.) but does not provide specific details on how the training data itself is partitioned into training and validation sets for model development. |
| Hardware Specification | No | The paper states: "We train the model with 8 synchronized graphic processing units (GPUs)" but does not specify the exact GPU models, CPU, or other hardware specifications used. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or other libraries). |
| Experiment Setup | Yes | We use an initial learning rate of 0.01 and reduce the learning rate by 0.1 at 50k, 70k and 80k with a weight decay of 0.0005 and a momentum of 0.9 using stochastic gradient descent (SGD). We train the model with 8 synchronized graphic processing units (GPUs) and a mini-batch, including 128 images per GPU. We empirically set λmeta = 0.5, λdisen = 1.0 and λflt = 1.0, respectively. The margin m is empirically set to 0.4. |