JingyaoLi/

MOODv2

🏠 MOODv2

• 🤗 Model • 🐱 Code • 📃 MOODv1 • 📃 MOODv2

The crux of effective out-of-distribution (OOD) detection lies in acquiring a robust in-distribution (ID) representation, distinct from OOD samples. While previous methods predominantly leaned on recognition-based techniques for this purpose, they often resulted in shortcut learning, lacking comprehensive representations. In our study, we conducted a comprehensive analysis, exploring distinct pretraining tasks and employing various OOD score functions. The results highlight that the feature representations pre-trained through reconstruction yield a notable enhancement and narrow the performance gap among various score functions. This suggests that even simple score functions can rival complex ones when leveraging reconstruction-based pretext tasks. Reconstruction-based pretext tasks adapt well to various score functions. As such, it holds promising potential for further expansion. Our OOD detection framework, MOODv2, employs the masked image modeling pretext task. Without bells and whistles, MOODv2 impressively enhances 14.30% AUROC to 95.68% on ImageNet and achieves 99.98% on CIFAR-10.

To predict an input image is in-distribution or out-of-distribution, we support the following OOD detection methods:

For the example OOD image imgs/DTD_cracked_0004.jpg, you are supposed to get:

MSP  evaluation:   out-of-distribution 
MaxLogit  evaluation:   out-of-distribution 
Energy  evaluation:   out-of-distribution 
Energy+React  evaluation:   out-of-distribution 
ViM  evaluation:   out-of-distribution 
Residual  evaluation:   out-of-distribution 
GradNorm  evaluation:   out-of-distribution 
Mahalanobis  evaluation:   out-of-distribution

To reproduce the results in our paper, please refer to our repository for details.