Author name cluster

Yunbing Xing

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

ECAI Conference 2024 Conference Paper

Cascade Memory for Unsupervised Anomaly Detection

Jiahao Li 0007
Yiqiang Chen 0001
Yunbing Xing
Yang Gu 0001
Xiangyuan Lan

Unsupervised anomaly detection is to detect previously unseen rare samples without any prior knowledge about them. With the emergence of deep learning, many methods employ normal data reconstruction to train detection models, which is expected to yield relatively large errors when reconstructing anomalies. However, recent studies find that anomalies can be overgeneralized, resulting in reconstruction errors as small as normal samples. In this paper, we examine the anomaly overgeneralization problem and propose global semantic information learning. Normal and anomalous samples may share the same local feature such as textures, edges, and corners, but have separability at the global semantic level. To address this, we propose a novel cascade memory architecture designed to capture global semantic information in the latent space and introduce a configurable sparsification and random forgetting mechanism. Our proposed method achieves state-of-the-art experimental results on different public benchmarks, without the introduction of any additional auxiliary loss terms. The code is available at https: //github. com/LiJiahao-Alex/Cascade-Memory.

Details

UAI Conference 2023 Conference Paper

Memory Mechanism for Unsupervised Anomaly Detection

Jiahao Li 0007
Yiqiang Chen 0001
Yunbing Xing

Unsupervised anomaly detection is a binary classification that detects anomalies in unseen samples given only unlabeled normal data. Reconstruction-based approaches are widely used, which perform reconstruction error minimization on training data to learn normal patterns and quantify the degree of anomalies by reconstruction errors on testing data. However, this approach tends to miss anomalies when the normal data has multi-pattern. Because the model generalizes unrestrictedly beyond normal patterns even to include anomaly patterns. In this paper, we proposed a memory mechanism that memorizes typical normal patterns through a capacity-controlled external differentiable matrix so that the generalization of the model to anomalies is limited by the retrieval of the matrix. We achieved state-of-the-art performance on several public benchmarks.

Details