---
language: en
license: mit
library_name: pytorch
tags:
- face-recognition
- self-supervised-learning
- contrastive-learning
datasets:
- YFCC-CelebA
- CelebA
---

VCL: Variational Contrastive Learning for Face Understanding

VCL is a robust self-supervised learning method designed specifically for face understanding tasks, combining variational contrastive learning with beta-divergence to effectively handle noisy and unlabeled datasets[1].

Model Details

Model Description

Developed by: Mehmet Can Yavuz and Berrin Yanikoglu Model type: Self-Supervised Variational Contrastive Learning with Applications to Face Understanding Language(s): Python License: MIT Model: ResNet10t

Uses

Direct Use

The model is designed for:

Face attribute recognition
Face verification tasks
Multi-label classification problems
Learning from noisy and unlabeled datasets

Model Architecture

The architecture consists of three main components:

Feature extraction backbone (ResNet10t or VGG11bn)
Gaussian sampling head for distribution learning
Contrastive learning framework with augmentations

Training Details

Training Data

The model was pretrained on the YFCC-CelebA dataset and you can fine-tune on CelebA dataset.

Training Procedure

Training Hyperparameters

Training regime:

Optimizer: AdamW
Learning rate: 1e-3
Weight decay: 0.01
Batch size: 128
Temperature: 0.07
Beta: 0.005

Evaluation

Results

Performance on CelebA test set with different pretraining approaches:

Setting	ResNet10t (1%)	VGG11bn (1%)	ResNet10t (10%)	VGG11bn (10%)
VCL	0.5836	0.5719	0.6848	0.6796
VCL (beta)	0.5998	0.5958	0.7098	0.6998

How to Get Started with the Model

# Installation
git clone https://github.com/convergingmachine/VCL
cd VCL
pip install -r requirements.txt

# Training
python train_beta.py


## Citation

```bibtex
@INPROCEEDINGS{10582001,
  author={Yavuz, Mehmet Can and Yanikoglu, Berrin},
  booktitle={2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG)}, 
  title={Self-Supervised Variational Contrastive Learning with Applications to Face Understanding}, 
  year={2024},
  pages={1-9},
  doi={10.1109/FG59268.2024.10582001}}

Model Card Contact

For questions about this model, please open an issue in the GitHub repository.