Awesome

Rank-N-Contrast: Learning Continuous Representations for Regression

Rank-N-Contrast: Learning Continuous Representations for Regression
Kaiwen Zha*, Peng Cao*, Jeany Son, Yuzhe Yang, Dina Katabi (*equal contribution)
NeurIPS 2023 (Spotlight)

Loss Function

The loss function RnCLoss in loss.py takes features and labels as input, and return the loss value.

from loss import RnCLoss

# define loss function with temperature, label difference measure, 
# and feature similarity measure
criterion = RnCLoss(temperature=2, label_diff='l1', feature_sim='l2')

# features: [bs, 2, feat_dim]
features = ...
# labels: [bs, label_dim]
labels = ...

# compute RnC loss
loss = criterion(features, labels)

Running

Download AgeDB dataset from here and extract the zip file (you may need to contact the authors of AgeDB dataset for the zip password) to folder ./data.

To train the model with the L1 loss, run
```
python main_l1.py
```
To train the model with the RnC framework, first run
```
python main_rnc.py
```
to train the encoder. The checkpoint of the encoder will be saved to ./save. Then, run
```
python main_linear.py --ckpt <PATH_TO_THE_TRAINED_ENCODER_CHECKPOINT>
```
to train the regressor.

Model Checkpoints

The checkpoints of the encoder and the regressor trained on AgeDB dataset are available here.

Citation

If you use this code for your research, please cite our paper:

@inproceedings{zha2023rank,
    title={Rank-N-Contrast: Learning Continuous Representations for Regression},
    author={Zha, Kaiwen and Cao, Peng and Son, Jeany and Yang, Yuzhe and Katabi, Dina},
    booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
    year={2023}
}