Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction
This work addresses the challenge of encoding contextual information for visual processing, which is incremental as it builds on predictive coding frameworks with neurophysiological consistency.
The authors tackled the problem of learning global contextual relationships in visual events to improve perceptual inference, interpolation, and prediction, resulting in a model that outperforms gated Boltzmann machines in contextual estimation and achieves state-of-the-art prediction accuracy across various tasks.
We propose a new neurally-inspired model that can learn to encode the global relationship context of visual events across time and space and to use the contextual information to modulate the analysis by synthesis process in a predictive coding framework. The model learns latent contextual representations by maximizing the predictability of visual events based on local and global contextual information through both top-down and bottom-up processes. In contrast to standard predictive coding models, the prediction error in this model is used to update the contextual representation but does not alter the feedforward input for the next layer, and is thus more consistent with neurophysiological observations. We establish the computational feasibility of this model by demonstrating its ability in several aspects. We show that our model can outperform state-of-art performances of gated Boltzmann machines (GBM) in estimation of contextual information. Our model can also interpolate missing events or predict future events in image sequences while simultaneously estimating contextual information. We show it achieves state-of-art performances in terms of prediction accuracy in a variety of tasks and possesses the ability to interpolate missing frames, a function that is lacking in GBM.