To tackle this issue, we propose to explore the predictive coding network, so called PredNet, a recurrent neural network that propagates predictive coding errors across layers and time steps. We analyze whether PredNet can better capture motions in videos by estimating over time the representations ...
PredNet --- Deep Predictive coding networks for video prediction and unsupervised learning ICLR 2017 2017.03.12 Code and video examples can be found at:https://coxlab.github.io/prednet/ 摘要:基于监督训练的深度学习技术取得了非常大的成功,但是无监督问题仍然是一个未能解决的一大难题(从未标注的数据中...
We introduce PrediRep, a novel recurrent convolutional neural network that faithfully reproduces functional features of cortical processing as proposed by predictive coding while maintaining competitive performance on machine vision tasks. This renders PrediRep well suited for investigating cortical phenomena ...
Predictive Coding in the Visual Cortex by a Recurrent Network with Gabor Receptive Fields作者:Gustavo Deco, Bernd Schürmann 摘要 We derive a recurrent neural network architecture of single cells in the primary visual cortex that dynamically improves a 2D-Gabor wavelet based representation of an image...
Due to the need to iterate the vs until convergence, the predictive coding network had roughly a 100x greater computational cost than the backprop network. This seems to imply that artificial NNs are 100x more computationally efficient (at the cost of not being able to grow and probably lower ...
According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no p
Code and models accompanyingDeep Predictive Coding Networks for Video Prediction and Unsupervised Learningby Bill Lotter, Gabriel Kreiman, and David Cox. The PredNet is a deep recurrent convolutional neural network that is inspired by the neuroscience concept of predictive coding (Rao and Ballard, 1999...
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This coding of errors by the forward flow of information in the network entails increasing sparsity of (feedforward) neural activity in proportion to the accuracy of learned representations of the environment at higher layers in the hierarchy (under the assumption that feedback connections act only ...
It severely limits the network depth due to learning stagnation. Here, we prove why this bottleneck occurs. We then propose a new forward-inference strategy based on accelerated proximal gradients. This strategy has faster theoretical convergence guarantees than the one used for DPCNs. It overcomes...