MIT News  May 9, 2023 Unlike humans, computer vision models don’t typically exhibit perceptual straightness, so they learn to represent visual information in a highly unpredictable way. But if machine-learning models had this ability, it might enable them to better estimate how objects or people will move. Researchers at MIT explored the relationship between network architecture, differing types of robustness, biologically-inspired filtering mechanisms, and representational straightness in response to time-varying input; they identified strengths and limitations of straightness as a useful way of evaluating neural network representations. They found that adversarial training leads to straighter representations in both convolutional neural […]