3 accepted papers
Unsupervised industrial anomaly detection requires accurately identifying defects without labeled data. Traditional autoencoder-based methods often struggle with incomplete anomaly suppression and loss of fine details, as their single-pass decoding fails to effectively handle anomalies with varying
Machine learning algorithms often struggle to eliminate inherent data biases, particularly those arising from unreliable labels, which poses a significant challenge in ensuring fairness. Existing fairness techniques that address label bias typically involve modifying models and intervening in the tr…
In learning with fairness, for every instance, its label can be randomly flipped to another class due to the practitioner’s prejudice, namely, label bias. The existing well-studied fair representation learning methods focus on removing the dependency between the sensitive factors and the input data,…