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Found 8 papers, 3 papers with code
Finding Dino: A plug-and-play framework for unsupervised detection of out-of-distribution objects using prototypes
It is an inference-based method that does not require training on the domain dataset and relies on extracting relevant features from self-supervised pre-trained models.
Enhancing Interpretability of Vertebrae Fracture Grading using Human-interpretable Prototypes
In this work, we propose a novel interpretable-by-design method, ProtoVerse, to find relevant sub-parts of vertebral fractures (prototypes) that reliably explain the model's decision in a human-understandable way.
Preventing Errors in Person Detection: A Part-Based Self-Monitoring Framework
The ability to detect learned objects regardless of their appearance is crucial for autonomous systems in real-world applications.
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection
As the use of machine learning continues to expand, the importance of ensuring its safety cannot be overstated.
Towards Human-Interpretable Prototypes for Visual Assessment of Image Classification Models
Explaining black-box Artificial Intelligence (AI) models is a cornerstone for trustworthy AI and a prerequisite for its use in safety critical applications such that AI models can reliably assist humans in critical decisions.
Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space
It is essential for safety-critical applications of deep neural networks to determine when new inputs are significantly different from the training distribution.
OODformer: Out-Of-Distribution Detection Transformer
A serious problem in image classification is that a trained model might perform well for input data that originates from the same distribution as the data available for model training, but performs much worse for out-of-distribution (OOD) samples.
From Black-box to White-box: Examining Confidence Calibration under different Conditions
Based on previous work, we study the miscalibration of object detection models with respect to image location and box scale.
