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MIT researchers have discovered that a specific training method called adversarial training can help computer vision models learn more perceptually straight representations, like humans do. Training involves showing machine-learning model millions of examples so it can learn a task. The researchers found that training computer vision models using this technique improves the models’ perceptual straightness. The team also discovered that perceptual straightness is affected by the task one trains a model to perform. By gaining a better understanding of perceptual straightness in computer vision, the researchers hope to uncover insights that could help them develop models that make more accurate predictions.