9,688 research outputs found
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation
The primate brain contains a hierarchy of visual areas, dubbed the ventral
stream, which rapidly computes object representations that are both specific
for object identity and relatively robust against identity-preserving
transformations like depth-rotations. Current computational models of object
recognition, including recent deep learning networks, generate these properties
through a hierarchy of alternating selectivity-increasing filtering and
tolerance-increasing pooling operations, similar to simple-complex cells
operations. While simulations of these models recapitulate the ventral stream's
progression from early view-specific to late view-tolerant representations,
they fail to generate the most salient property of the intermediate
representation for faces found in the brain: mirror-symmetric tuning of the
neural population to head orientation. Here we prove that a class of
hierarchical architectures and a broad set of biologically plausible learning
rules can provide approximate invariance at the top level of the network. While
most of the learning rules do not yield mirror-symmetry in the mid-level
representations, we characterize a specific biologically-plausible Hebb-type
learning rule that is guaranteed to generate mirror-symmetric tuning to faces
tuning at intermediate levels of the architecture
Learning the Irreducible Representations of Commutative Lie Groups
We present a new probabilistic model of compact commutative Lie groups that
produces invariant-equivariant and disentangled representations of data. To
define the notion of disentangling, we borrow a fundamental principle from
physics that is used to derive the elementary particles of a system from its
symmetries. Our model employs a newfound Bayesian conjugacy relation that
enables fully tractable probabilistic inference over compact commutative Lie
groups -- a class that includes the groups that describe the rotation and
cyclic translation of images. We train the model on pairs of transformed image
patches, and show that the learned invariant representation is highly effective
for classification
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