AgeFlow: Conditional Age Progression and Regression with Normalizing Flows
Authors: Zhizhong Huang, Shouzhen Chen, Junping Zhang, Hongming Shan
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate superior performance over existing GANs-based methods on two benchmarked datasets. |
| Researcher Affiliation | Academia | 1Shanghai Key Lab of Intelligent Information Processing, School of Computer Science Fudan University, Shanghai 200433, China 2Institute of Science and Technology for Brain-inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200431, China 3Shanghai Center for Brain Science and Brain-inspired Technology, Shanghai 200031, China |
| Pseudocode | No | The paper describes the network architecture and loss functions in detail but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available at https://github.com/Hzzone/AgeFlow. |
| Open Datasets | Yes | We conducted experiments on two benchmark age datasets: MORPH [Ricanek and Tesafaye, 2006] and CACD [Chen et al., 2015]. We also adopted FG-NET and Celeb A [Liu et al., 2015] as external testing sets... |
| Dataset Splits | No | We randomly divided the dataset into two parts without identities overlapping: 80% for training and the remaining for testing. |
| Hardware Specification | Yes | We trained all models with a batch size of 16 on 4 NVIDIA V100 GPUs |
| Software Dependencies | No | The paper mentions 'Py Torch' and 'Adam optimizer' but does not specify their version numbers. |
| Experiment Setup | Yes | All models are implemented by Py Torch and trained by Adam optimizer with a fixed learning rate of 10 5 for the GLOW model and ICTM, and 10 4 for the discriminator. In addition, due to the limited GPU memory, we trained all models with a batch size of 16 on 4 NVIDIA V100 GPUs and the parameters are updated for every 4 iterations, equal to a batch size of 64. We first trained the GLOW model on Celeb A [Liu et al., 2015] thanks to its diversity and a huge amount of face images for 1M iterations and then finetuned the models on each dataset with only 50K iterations. ICTM contains m = 32 flows. The hyperparameters in the final loss are empirically set as follows: λal was 1; λcl was 0.01; λacl was 1; λD acl was 0.1; and λakl was 1. The s in the knowledge distilling loss is set as 1.4 for MORPH and 1.8 for CACD. |