Shape from shading
Computation of component image velocity from local phase information
International Journal of Computer Vision
A general framework for parallel distributed processing
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Learning invariance from transformation sequences
Neural Computation
Discovering viewpoint-invariant relationships that characterize objects
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
1994 Special Issue: Modeling visual recognition from neurobiological constraints
Neural Networks - Special issue: models of neurodynamics and behavior
1994 Special Issue: A fast dynamic link matching algorithm for invariant pattern recognition
Neural Networks - Special issue: models of neurodynamics and behavior
Phase-based binocular vergence control and depth reconstruction using active vision
CVGIP: Image Understanding
Spatial coherence as an internal teacher for a neural network
Backpropagation
The nature of statistical learning theory
The nature of statistical learning theory
Unsupervised learning of temporal constancies by pyramidal-type neurons
MANNA '95 Proceedings of the first international conference on Mathematics of neural networks : models, algorithms and applications: models, algorithms and applications
Self-organization of shift-invariant receptive fields
Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Learning to Categorize Objects Using Temporal Coherence
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Receptive Fields Similar to Simple Cells Maximize Temporal Coherence in Natural Video
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
An Adaptive Hierarchical Model of the Ventral Visual Pathway Implemented on a Mobile Robot
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Learning the nonlinearity of Neurons from natural visual stimuli
Neural Computation
Slow feature analysis: a theoretical analysis of optimal free responses
Neural Computation
Modeling the adaptive visual system: a survey of principled approaches
Neural Networks - Special issue: Neuroinformatics
Learning Viewpoint Invariant Perceptual Representations from Cluttered Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bilinear Sparse Coding for Invariant Vision
Neural Computation
2006 Special issue: Exploratory analysis of climate data using source separation methods
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Independent Slow Feature Analysis and Nonlinear Blind Source Separation
Neural Computation
A Maximum-Likelihood Interpretation for Slow Feature Analysis
Neural Computation
Learning the Lie Groups of Visual Invariance
Neural Computation
A principle for learning egocentric-allocentric transformation
Neural Computation
Unsupervised slow subspace-learning from stationary processes
Theoretical Computer Science
Invariant Object Recognition with Slow Feature Analysis
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Deep learning from temporal coherence in video
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Steps to a Cyber-Physical Model of Networked Embodied Anticipatory Behavior
Anticipatory Behavior in Adaptive Learning Systems
On the prospects for building a working model of the visual cortex
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Learning Deep Architectures for AI
Foundations and Trends® in Machine Learning
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Learning from Examples to Generalize over Pose and Illumination
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Slow feature discriminant analysis and its application on handwritten digit recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Autoregressive model of the hippocampal representation of events
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
Biologically inspired posture recognition and posture change detection for humanoid robots
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
A spiking neuron as information bottleneck
Neural Computation
Colored subspace analysis: dimension reduction based on a signal's autocorrelation structure
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Gender and age estimation from synthetic face images
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
A bilinear model for consistent topographic representations
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Learning invariant visual shape representations from physics
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
The slowness principle: SFA can detect different slow components in non-stationary time series
International Journal of Innovative Computing and Applications
Nonlinear dimensionality reduction using a temporal coherence principle
Information Sciences: an International Journal
A multifactor winner-take-all dynamics
Neural Computation
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Regularized sparse Kernel slow feature analysis
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Learning Topographic Representations of Nature Images with Pairwise Cumulant
Neural Processing Letters
Keep breathing! common motion helps multi-modal mapping
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Maximum contrast analysis for nonnegative blind source separation
Computers & Mathematics with Applications
Invariant object recognition and pose estimation with slow feature analysis
Neural Computation
A differential model of the complex cell
Neural Computation
Unsupervised slow subspace-learning from stationary processes
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
Image representation in visual cortex and high nonlinear approximation
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Separation of nonlinear image mixtures by denoising source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
On the relation of slow feature analysis and laplacian eigenmaps
Neural Computation
Dynamic inputs and attraction force analysis for visual invariance and transformation estimation
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
A canonical correlation analysis based method for improving BSS of two related data sets
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Neural information processing with feedback modulations
Neural Computation
Incremental slow feature analysis
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Learning features and predictive transformation encoding based on a horizontal product model
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Incremental slow feature analysis with indefinite kernel for online temporal video segmentation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Deep learning of representations: looking forward
SLSP'13 Proceedings of the First international conference on Statistical Language and Speech Processing
Deep feature learning using target priors with applications in ECoG signal decoding for BCI
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Construction of approximation spaces for reinforcement learning
The Journal of Machine Learning Research
Improved sparse coding under the influence of perceptual attention
Neural Computation
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Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.