  AutoEncoders (AEs) are commonly used for machine learning tasks due to their
intrinsic learning ability. This unique characteristic can be capitalized for
Outlier Detection (OD). However conventional AE-based methods face the issue of
overconfident decisions and unexpected reconstruction results of outliers,
limiting their performance in OD. To mitigate these issues, the Mean Squared
Error (MSE) and Negative Logarithmic Likelihood (NLL) were firstly analyzed,
and the importance of incorporating aleatoric uncertainty to AE-based OD was
elucidated. Then the Weighted Negative Logarithmic Likelihood (WNLL) was
proposed to adjust for the effect of uncertainty for different OD scenarios.
Moreover, the Mean-Shift Scoring (MSS) method was proposed to utilize the local
relationship of data to reduce the issue of false inliers caused by AE.
Experiments on 32 real-world OD datasets proved the effectiveness of the
proposed methods. The combination of WNLL and MSS achieved 41% relative
performance improvement compared to the best baseline. In addition, MSS
improved the detection performance of multiple AE-based outlier detectors by an
average of 20%. The proposed methods have the potential to advance AE's
development in OD.
