CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection

The official implementation of CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection (ICML2024 CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection (openreview.net)).

Abstract

Recent vision-language pre-trained models (VL-PTMs) have shown remarkable success in open-vocabulary tasks. However, downstream use cases often involve further fine-tuning of VL-PTMs, which may distort their general knowledge and impair their ability to handle distribution shifts. In real-world scenarios, machine learning systems inevitably encounter both covariate shifts (e.g., changes in image styles) and semantic shifts (e.g., test-time unseen classes). This highlights the importance of enhancing out-of-distribution (OOD) generalization on covariate shifts and simultaneously detecting semantic-shifted unseen classes. Thus a critical but underexplored question arises: How to improve VL-PTMs' generalization ability to closed-set OOD data, while effectively detecting open-set unseen classes during fine-tuning? In this paper, we propose a novel objective function of OOD detection that also serves to improve OOD generalization. We show that minimizing the gradient magnitude of energy scores on training data leads to domain-consistent Hessians of classification loss, a strong indicator for OOD generalization revealed by theoretical analysis. Based on this finding, we have developed a unified fine-tuning framework that allows for concurrent optimization of both tasks. Extensive experiments have demonstrated the superiority of our method.

Pipeline

Overview of the CRoFT framework

How to Install

This code is built on top of the awesome Dassl and [CoOp](KaiyangZhou/CoOp: Prompt Learning for Vision-Language Models (IJCV'22, CVPR'22) (github.com))., run pip install -r requirements.txt under CRoFT/CoOp/ to install the required packages.

git clone https://github.com/LinLLLL/CRoFT
cd CRoFT/CoOp

conda create -n CRoFT python=3.9
conda activate CRoFT

pip install -r requirements.txt

# Install the according versions of torch and torchvision
conda install pytorch torchvision cudatoolkit

Datasets

Follow DATASET.md to install ImageNet, ImageNetV2, ImageNet-Sketch, ImageNet-A, ImageNet-R, and other 10 datasets referring to CoOp.

For the OOD datasets, such as PACS and VLCS, are publicly available but need to be downloaded manually. Please refer this [instruction](OoD-Bench/data/README.md at main · m-Just/OoD-Bench (github.com)) for OOD datasets download. Please make sure that the directory structure of each dataset is arranged as follows:

PACS

PACS
├── images
    ├── art_painting
    ├── cartoon
    ├── photo
    └── sketch
├── test_on_art_painting.json
├── test_on_cartoon.json
├── test_on_photo.json
└── test_on_sketch.json

VLCS

VLCS
├── images
    ├── CALTECH
    ├── LABELME
    ├── PASCAL
    └── SUN
├── test_on_caltech.json
├── test_on_labelme.json
├── test_on_pascal.json
└── test_on_sun.json

▶️ ▶️ ▶️ How to Run ▶️ ▶️ ▶️

We provide the running scripts in CoOp/scripts. We take CRoFT as an example, other methods can be similarly evaluated. Make sure you change the path on DATA in shell files under CoOp/scripts/CRoFT and run the commands under CoOp/scripts/CRoFT.

------------------------------ SETUP-I ------------------------------

For training CRoFT on the in-distribution ImageNet datasets:

python run_setup1.py

For evaluating CRoFT on the closed-set OOD datasets and open-set OOD datasets:

python test_setup1.py

For loading energy distribution of different types of datasets:

bash test_energy.sh

------------------------------ SETUP-II ------------------------------

For training CRoFT on the in-distribution PACS or VLCS datasets:

python run_setup2.py

After run_setup2.py, evaluation on the closed-set OOD datasets is also completed.

For evaluating CRoFT on the open-set OOD datasets:

python test_setup2_energy.py

▶️ ▶️ ▶️ Collect Results ▶️ ▶️ ▶️

For collecting CRoFT's OOD generalization results in SETUP-I:

# run the commands under CoOp/
python collect_result_set1_oodg.py

For collecting CRoFT's OOD detection results in SETUP-I:

# run the commands under CoOp/
python collect_result_set1_oodd.py

For collecting CRoFT's OOD detection results in SETUP-II:

# run the commands under CoOp/
python collect_result_set2_oodg.py

For collecting CRoFT's OOD detection results in SETUP-II:

We probide two OOD detection methods in SETUP-II, i.e., inferring energy score and KNN distance. Make sure you have completed the evluation process of python test_setup2_energy.py before you run python test_setup2_knn.py.

# run the commands under CoOp/
# inferring energy score
python collect_result_set2_oodd.py

# run the commands under CoOp/scripts/CRoFT
# inferring KNN distance:
python test_setup2_knn.py

The evaluation results are then saved to the folders output and eval_open_ood or displayed directly on your screen.

Acknowledgement

This repo benefits from CLIP, CoOp [CoCoOp](KaiyangZhou/CoOp: Prompt Learning for Vision-Language Models (IJCV'22, CVPR'22) (github.com)), [Tip-Adapter-F](gaopengcuhk/Tip-Adapter (github.com)), [DPLCLIP](shogi880/DPLCLIP (github.com)), CLIP-Adapter and the OOD generalization benchmark [OoD-Bench](ynysjtu/ood_bench (github.com)). Thanks for their wonderful works.

Citation

If you use this code in your research, please kindly cite this paper:

@article{zhu2024croft,
  title={CRoFT: Robust Fine-Tuning with Concurrent Optimization for OOD Generalization and Open-Set OOD Detection},
  author={Zhu, Lin and Yang, Yifeng and Gu, Qinying and Wang, Xinbing and Zhou, Chenghu and Ye, Nanyang},
  journal={arXiv preprint arXiv:2405.16417},
  year={2024}
} 

Contact ✉️

If you have any question about this project, please feel free to contact [email protected].