相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Learning Intermediate-Level Representations of Form and Motion from Natural Movies
Charles F. Cadieu et al.
NEURAL COMPUTATION (2012)
How Does the Brain Solve Visual Object Recognition?
James J. DiCarlo et al.
NEURON (2012)
A Theory of Slow Feature Analysis for Transformation-Based Input Signals with an Application to Complex Cells
Henning Sprekeler et al.
NEURAL COMPUTATION (2011)
Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex
Nuo Li et al.
NEURON (2010)
Emergence of complex cell properties by learning to generalize in natural scenes
Yan Karklin et al.
NATURE (2009)
A Structured Model of Video Reproduces Primary Visual Cortical Organisation
Pietro Berkes et al.
PLOS COMPUTATIONAL BIOLOGY (2009)
Unsupervised natural experience rapidly alters invariant object representation in visual cortex
Nuo Li et al.
SCIENCE (2008)
Unsupervised slow subspace-learning from stationary processes
Andreas Maurer
THEORETICAL COMPUTER SCIENCE (2008)
Excitatory and suppressive receptive field subunits in awake monkey primary visual cortex (V1)
Xiaodong Chen et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2007)
A maximum-likelihood interpretation for slow feature analysis
Richard Turner et al.
NEURAL COMPUTATION (2007)
Unsupervised learning of a steerable basis for invariant image representations
Matthias Bethge et al.
HUMAN VISION AND ELECTRONIC IMAGING XII (2007)
Spatiotemporal elements of macaque V1 receptive fields
NC Rust et al.
NEURON (2005)
Learning viewpoint invariant perceptual representations from cluttered images
MW Spratling
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2005)
Slow feature analysis yields a rich repertoire of complex cell properties
P Berkes et al.
JOURNAL OF VISION (2005)
How are complex cell properties adapted to the statistics of natural stimuli?
KP Kording et al.
JOURNAL OF NEUROPHYSIOLOGY (2004)
Learning the nonlinearity of neurons from natural visual stimuli
C Kayser et al.
NEURAL COMPUTATION (2003)
Bubbles:: a unifying framework for low-level statistical properties of natural image sequences
A Hyvärinen et al.
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION (2003)
Simple-cell-like receptive fields maximize temporal coherence in natural video
J Hurri et al.
NEURAL COMPUTATION (2003)
Slow feature analysis: Unsupervised learning of invariances
L Wiskott et al.
NEURAL COMPUTATION (2002)
Learning the invariance properties of complex cells from their responses to natural stimuli
W Einhäuser et al.
EUROPEAN JOURNAL OF NEUROSCIENCE (2002)
Emergence of phase- and shift-invariant features by decomposition of natural images into independent feature subspaces
A Hyvärinen et al.
NEURAL COMPUTATION (2000)