Deep Predictive Coding Networks

The quality of data representation in deep learning methods is directly related to the prior model imposed on the representations; however, generally used fixed priors are not capable of adjusting to the context in the data. To address this issue, we propose deep predictive coding networks, a hierarchical generative model that empirically alters priors on the latent representations in a dynamic and context-sensitive manner. This model captures the temporal dependencies in time-varying signals and uses top-down information to modulate the representation in lower layers. The centerpiece of our model is a novel procedure to infer sparse states of a dynamic model which is used for feature extraction. We also extend this feature extraction block to introduce a pooling function that captures locally invariant representations. When applied on a natural video data, we show that our method is able to learn high-level visual features. We also demonstrate the role of the top-down connections by showing the robustness of the proposed model to structured noise.

[1]  Terrence J. Sejnowski,et al.  Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.

[2]  Eric A. Wan,et al.  Nonlinear estimation and modeling of noisy time series by dual kalman filtering methods , 2000 .

[3]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  Yurii Nesterov,et al.  Smooth minimization of non-smooth functions , 2005, Math. Program..

[5]  Yann LeCun,et al.  Efficient Learning of Sparse Invariant Representations , 2011, ArXiv.

[6]  Thomas Hofmann,et al.  Greedy Layer-Wise Training of Deep Networks , 2007 .

[7]  Xi Chen,et al.  Smoothing proximal gradient method for general structured sparse regression , 2010, The Annals of Applied Statistics.

[8]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[9]  Graham W. Taylor,et al.  Deconvolutional networks , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[11]  Marc'Aurelio Ranzato,et al.  Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition , 2010, ArXiv.

[12]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[13]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[14]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[15]  H. Zou,et al.  Addendum: Regularization and variable selection via the elastic net , 2005 .

[16]  Michael S. Lewicki,et al.  A Hierarchical Bayesian Model for Learning Nonlinear Statistical Regularities in Nonstationary Natural Signals , 2005, Neural Computation.

[17]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[18]  Karl J. Friston Hierarchical Models in the Brain , 2008, PLoS Comput. Biol..

[19]  Y-Lan Boureau,et al.  Learning Convolutional Feature Hierarchies for Visual Recognition , 2010, NIPS.

[20]  Justin K. Romberg,et al.  Sparsity penalties in dynamical system estimation , 2011, 2011 45th Annual Conference on Information Sciences and Systems.

[21]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[22]  Christophe Andrieu,et al.  Bayesian sequential compressed sensing in sparse dynamical systems , 2010, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[23]  G. Giannakis,et al.  Compressed sensing of time-varying signals , 2009, 2009 16th International Conference on Digital Signal Processing.

[24]  Rajesh P. N. Rao,et al.  Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex , 1997, Neural Computation.