Before revision:  Our experiments show that as a result of the substantial reduction of these biases, models trained on the filtered datasets yield better generalization to out-of-distribution tasks , especially when the benchmarks used for training are over-populated with biased samples.
After revision:  We present extensive supporting evidence that AFLite is broadly applicable for reduction of measurable dataset biases, and that models trained on the filtered datasets yield better generalization to out-of-distribution tasks , especially when the benchmarks used for training are over-populated with biased samples.