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Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data
Unsupervised anomaly detection methods are at the forefront of industrial anomaly detection efforts and have made notable progress.
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection
While these unsupervised anomaly detection methods offer convenience, they also overlook the crucial prior information embedded within anomalous samples.
Semantic Feature Integration network for Fine-grained Visual Classification
By eliminating unnecessary features and reconstructing the semantic relations among discriminative features, our SFI-Net has achieved satisfying performance.
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