https://github.com/kaiwenzha/Rank-N-Contrast

[NeurIPS 2023, Spotlight] Rank-N-Contrast: Learning Continuous Representations for Regression
https://github.com/kaiwenzha/Rank-N-Contrast

continuity contrastive-learning neurips neurips-2023 ranking-algorithms regression representation-learning

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[NeurIPS 2023, Spotlight] Rank-N-Contrast: Learning Continuous Representations for Regression

• Host: GitHub
• URL: https://github.com/kaiwenzha/Rank-N-Contrast
• Owner: kaiwenzha
• Created: 2023-10-09T18:46:26.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-28T18:53:41.000Z (8 months ago)
• Last Synced: 2024-05-13T22:53:06.960Z (6 months ago)
• Topics: continuity, contrastive-learning, neurips, neurips-2023, ranking-algorithms, regression, representation-learning
• Language: Python
• Homepage:
• Size: 4.07 MB
• Stars: 59
• Watchers: 1
• Forks: 7
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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## Rank-N-Contrast: Learning Continuous Representations for Regression

[Paper](https://arxiv.org/abs/2210.01189) | [Talk](https://youtu.be/T7TCBDmxMO0?si=l4SBKnIu26k9uqVH) | [Slides](assets/slides.pdf) | [BibTex](assets/bibtex.txt)



Rank-N-Contrast: Learning Continuous Representations for Regression\

[Kaiwen Zha](https://people.csail.mit.edu/kzha/)\*, [Peng Cao](https://people.csail.mit.edu/pengcao/)\*, [Jeany Son](https://jeanyson.github.io/), [Yuzhe Yang](https://www.mit.edu/~yuzhe/), [Dina Katabi](http://people.csail.mit.edu/dina/) (*equal contribution)\

_NeurIPS 2023 (Spotlight)_

### Loss Function

The loss function [`RnCLoss`](./loss.py#L34) in [`loss.py`](./loss.py) takes `features` and `labels` as input, and return the loss value.

```python

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](https://ibug.doc.ic.ac.uk/resources/agedb/) 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 

    ```

  to train the regressor.

### Model Checkpoints

The checkpoints of the encoder and the regressor trained on AgeDB dataset are available [here](https://drive.google.com/file/d/11_W-wArbk5lgTCKyJY0fsPoALbx_Qkno/view?usp=sharing).

### Citation

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

```bibtex

@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}

}

```