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Due to the scarcity of reliable anomaly labels, recent anomaly detection methods leveraging noisy auto-generated labels either select clean samples or refurbish noisy labels. However, both approaches struggle due to the unique properties of anomalies.Sample selectionoften fails to separate sufficiently many clean anomaly samples from noisy ones, whilelabel refurbishmenterroneously refurbishesmarginalclean samples. To overcome these limitations, we design Unity, thefirstlearning from noisy labels (LNL) approach for anomaly detection that elegantly leverages the merits of both sample selection and label refurbishment to iteratively prepare a diverse clean sample set for network training. Unity uses a pair of deep anomaly networks to collaboratively select samples with clean labels based on prediction agreement, followed by a disagreement resolution mechanism to capture marginal samples with clean labels. Thereafter, Unity utilizes unique properties of anomalies to design an anomaly-centric contrastive learning strategy that accurately refurbishes the remaining noisy labels. The resulting set, composed ofselected and refurbishedclean samples, will be used to train the anomaly networks in the next training round. Our experimental study on 10 real-world benchmark datasets demonstrates that Unity consistently outperforms state-of-the-art LNL techniques by up to 0.31 in F-1 Score (0.52 \rightarrow 0.83).
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This content will become publicly available on February 26, 2026
MENTOR: Human Perception-Guided Pretraining for Increased Generalization
Leveraging human perception into training of convo- lutional neural networks (CNN) has boosted generalization capabilities of such models in open-set recognition tasks. One of the active research questions is where (in the model architecture or training pipeline) and how to efficiently incorporate always-limited human perceptual data into training strategies of models. In this paper, we introduce MENTOR (huMan pErceptioN-guided preTraining fOr increased geneRalization), which addresses this ques- tion through two unique rounds of training CNNs tasked with open-set anomaly detection. First, we train an au- toencoder to learn human saliency maps given an input image, without any class labels. The autoencoder is thus tasked with discovering domain-specific salient features which mimic human perception. Second, we remove the decoder part, add a classification layer on top of the encoder, and train this new model conventionally, now using class labels. We show that MENTOR successfully raises the generalization performance across three different CNN backbones in a variety of anomaly detection tasks (demonstrated for detection of unknown iris presentation attacks, synthetically-generated faces, and anomalies in chest X-ray images) compared to traditional pretraining methods (e.g., sourcing the weights from ImageNet), and as well as state- of-the-art methods that incorporate human perception guidance into training. In addition, we demonstrate that MENTOR can be flexibly applied to existing human perception- guided methods and subsequently increasing their generalization with no architectural modifications.
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- Award ID(s):
- 2237880
- PAR ID:
- 10585037
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-1083-1
- Page Range / eLocation ID:
- 7470 to 7479
- Format(s):
- Medium: X
- Location:
- Tucson, AZ, USA
- Sponsoring Org:
- National Science Foundation
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