MOOD - Multi-level Out-of-distribution Detection
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Lin, Ziqian et al. “MOOD: Multi-level Out-of-distribution Detection.” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021): 15308-15318.
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Existing OODdetection methods commonly rely on a scoring function thatderives statistics from the penultimate layer or output layerof the neural network * show annotation
existing solutions require a full feedforward pass for anygiven test-time input and utilize a fixed amount of computa-tion * show annotation
computational cost of OOD detection can be ex-acerbated by the over-parameterization of neural networks,which nowadays have reached unprecedented depth and ca-pacity * show annotation
how canwe enable out-of-distribution detection that can adjust andsave computations adaptively on-the-fly * show annotation
adaptive OOD detection frameworkbased on intermediate classifier outputs * show annotation
exploits intermedi-ate classifier outputs for dynamic and efficient OOD infer-ence * show annotation
direct relationship be-tween the OOD data complexity and optimal exit level, andshow that easy OOD examples can be effectively detectedearly without propagating to deeper layers. * show annotation