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Found 6 papers, 2 papers with code
Improving Performance of Semi-Supervised Learning by Adversarial Attacks
Semi-supervised learning (SSL) algorithm is a setup built upon a realistic assumption that access to a large amount of labeled data is tough.
ODIM: an efficient method to detect outliers via inlier-memorization effect of deep generative models
This study aims to solve the unsupervised outlier detection problem where training data contain outliers, but any label information about inliers and outliers is not given.
SLIDE: a surrogate fairness constraint to ensure fairness consistency
As they have a vital effect on social decision makings, AI algorithms should be not only accurate and but also fair.
Learning fair representation with a parametric integral probability metric
That is, we derive theoretical relations between the fairness of representation and the fairness of the prediction model built on the top of the representation (i. e., using the representation as the input).
$L_q$ regularization for Fairness AI robust to sampling bias
To ensure fairness on test data, we develop computationally efficient learning algorithms robust to sampling bias.
INN: A Method Identifying Clean-annotated Samples via Consistency Effect in Deep Neural Networks
By carrying out various experiments, we demonstrate that the INN method resolves the shortcomings in the memorization effect successfully and thus is helpful to construct more accurate deep prediction models with training data with noisy labels.
