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Found 3 papers, 2 papers with code
From Biased Data to Unbiased Models: a Meta-Learning Approach
It is well known that large deep architectures are powerful models when adequately trained, but may exhibit undesirable behavior leading to confident incorrect predictions, even when evaluated on slightly different test examples.
Learning Unbiased Representations via Mutual Information Backpropagation
We are interested in learning data-driven representations that can generalize well, even when trained on inherently biased data.
Generative Pseudo-label Refinement for Unsupervised Domain Adaptation
We exploit this finding in an iterative procedure where a generative model and a classifier are jointly trained: in turn, the generator allows to sample cleaner data from the target distribution, and the classifier allows to associate better labels to target samples, progressively refining target pseudo-labels.
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