A new arXiv preprint examines using counterfactual image generation to augment training data for medical image analysis. The authors argue that biased datasets produce biased models with limited clinical usefulness, and that synthetic counterfactual images can help offset those biases. The work is announced as a new submission in the cs.LG category.
A new preprint on arXiv studies why a teacher model's bias can still pass to a student model even when the training data has had all overt mentions of that bias removed. The authors report that no data-level defense tested so far reliably detects or eliminates this residual, or "phantom," transfer. The work frames the phenomenon as a dataset-level problem rooted in subtle statistical traces rather than explicit labels.
A new machine learning study argues that protein-protein interaction databases carry study and technical biases that skew protein and interaction attributes, letting models succeed by exploiting shortcuts rather than genuine biological signals. The authors propose methods to audit these datasets for such biases and to mitigate them, aiming for models that learn real biology instead of dataset artifacts.