arXiv:2208.00963
공식 코드 · Papers with Code
FrOoDo: Framework for Out-of-Distribution Detection
원문 초록
FrOoDo is an easy-to-use and flexible framework for Out-of-Distribution detection tasks in digital pathology. It can be used with PyTorch classification and segmentation models, and its modular design allows for easy extension. The goal is to automate the task of OoD Evaluation such that research can focus on the main goal of either designing new models, new methods or evaluating a new dataset. The code can be found at https://github.com/MECLabTUDA/FrOoDo.
함께 살펴볼 논문
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FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection
EviNet: Evidential Reasoning Network for Resilient Graph Learning in the Open and Noisy Environments
Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentation
Kernel PCA for Out-of-Distribution Detection: Non-Linear Kernel Selections and Approximations
GEM: Gaussian Embedding Modeling for Out-of-Distribution Detection in GUI Agents
Unsupervised Out-of-Distribution Detection in Medical Imaging Using Multi-Exit Class Activation Maps and Feature Masking
토론
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