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We introduce here a predictive coding based model that aims to generateaccurate and sharp future frames. Inspired by the predictive coding hypothesisand related works, the total model is updated through a combination ofbottom-up and top-down information flows, which can enhance the interactionbetween different network levels. Most importantly, We propose and improveseveral artifacts to ensure that the neural networks generate clear and naturalframes. Different inputs are no longer simply concatenated or added, they arecalculated in a modulated manner to avoid being roughly fused. The downsamplingand upsampling modules have been redesigned to ensure that the network can moreeasily construct images from Fourier features of low-frequency inputs.Additionally, the training strategies are also explored and improved togenerate believable results and alleviate inconsistency between the inputpredicted frames and ground truth. Our proposals achieve results that betterbalance pixel accuracy and visualization effect.

Anti-aliasing Predictive Coding Network for Future Video Frame Prediction