Mitigating Negative Transfer via Reducing Environmental Disagreement

This research addresses negative transfer, which occurs when an AI model performs worse after trying to apply knowledge from one domain (a labeled dataset) to a different domain (an unlabeled dataset) due to significant differences between them. The study identifies that models relying too heavily on non-causal environmental features (irrelevant details that don't actually cause predictions) creates disagreement across domains, harming performance. The proposed solution, called RED (Reducing Environmental Disagreement), separates each sample into causal features (the truly relevant information) and non-causal environmental features, then reduces the disagreement between domains based on these environmental features.