arXiv:2107.13304
공식 코드 · Papers with Code
Bayesian Autoencoders: Analysing and Fixing the Bernoulli likelihood for Out-of-Distribution Detection
원문 초록
After an autoencoder (AE) has learnt to reconstruct one dataset, it might be expected that the likelihood on an out-of-distribution (OOD) input would be low. This has been studied as an approach to detect OOD inputs. Recent work showed this intuitive approach can fail for the dataset pairs FashionMNIST vs MNIST. This paper suggests this is due to the use of Bernoulli likelihood and analyses why this is the case, proposing two fixes: 1) Compute the uncertainty of likelihood estimate by using a Bayesian version of the AE. 2) Use alternative distributions to model the likelihood.
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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