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Learning Invariant Features Using Inertial Priors
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Google's neocortex-inspired model learns invariant features from visual input by focusing on slowly changing patterns, promising new AI capabilities beyond current discriminative methods.
Key Insights
The model is a hierarchical Bayesian network that breaks down complex visual processing into modular component networks, each implementing variable-order Markov models.
It draws inspiration from multiple theories of the visual cortex, including hierarchical organization, Bayesian inference, temporal prediction, and continuous adaptation.
The framework aims to capture invariant features through 'slow feature analysis,' focusing on patterns that persist and remain stable across space and time.
The approach utilizes a process involving learning distinct spatiotemporal patterns, modeling their dynamics, and then grouping them into slowly moving features.
Google's interest is driven by the need for unsupervised learning methods for image understanding tasks like spam filtering and pornography detection, which current discriminative methods struggle with.
The model is designed to be distributed across many processors, with subnets communicating through shared variables, allowing for computation on a single core and scaling to large numbers of cores.
Google's motivation for neocortex-inspired AI
The talk frames the research within Google's potential interest in neocortex-like computational models, drawing an analogy to why General Motors might invest in the extruded plastics business: because they use a lot of it. Google's primary need for advanced visual understanding, for tasks such as detecting pornography and filtering spam, drives their interest in enabling technologies at the early stages of development. Current methods often rely on discriminative, supervised learning, which is effective when large amounts of labeled data are available. However, the neocortex model focuses on unsupervised learning, aiming to generate concepts and hierarchies that may go beyond human intuition. The model also aligns with Google's broader interests in content-addressable memory, coincidence-driven associations, sequence-based processing (increasingly important for text and video), and multi-modal inference.
Core computational principles inspired by the visual cortex
The proposed model is a hierarchical Bayesian network, structured into modular component networks that implement variable-order Markov models. Each component network is associated with a receptive field that maps to components in the level below it. The network's architecture is inspired by key observations of the neocortex: it is hierarchical, processing concepts at increasing levels of abstraction; it is Bayesian, probabilistically modeling dependencies; it is stochastic and generative, capable of producing realistic stimuli; it is temporal and predictive, anticipating future states; and it is continuously adaptive. This multi-faceted approach seeks to replicate the brain's robustness and efficiency in visual processing, moving beyond simple feature detection to true pattern recognition and completion, even for occluded objects. The use of learned invariants aims to reduce the need for extensive iterative computations typical in many AI search algorithms.
Deconstructing visual processing: from simple to complex cells and receptive fields
The model incorporates concepts from neuroscience, such as the structure of cortical columns and receptive fields. Columns, particularly 'hypercolumns' (around 60,000 neurons), are considered fundamental functional units. Receptive fields, initially small portions of the retina mapping to a cell, expand in size as information travels deeper into the ventral visual pathway (areas V1, V2, V4). This pathway is crucial for 'what' recognition, identifying features of objects rather than their spatial location. The distinction between simple and complex cells, introduced by Hubel and Wiesel, is also key. Simple cells respond to specific orientations and positions of stimuli within their receptive field. Complex cells, however, exhibit invariance to the precise location of the stimulus within their receptive field, responding as long as the correct orientation is present. This concept of learned invariance is a cornerstone of the model, allowing for robust recognition despite variations in input.
The role of slow feature analysis and invariant learning
A critical design element is the concept of 'slow feature analysis,' inspired by the work of Peter Foldiak and later byers and ciscnowski. The intuition is that to effectively perceive a rapidly changing world, the brain extracts representations that remain stable across space and time. These slowly changing features are more informative than transient signals. The model identifies these by first learning distinct spatiotemporal patterns within receptive fields (e.g., using mixtures of Gaussians or naive Bayes). It then models the dynamics of these patterns over time, learning a transition matrix. Finally, it analyzes these dynamics to group patterns into 'slowly moving features' that persist, providing a signature for stable aspects of the input, such as an object moving across a visual field regardless of its exact position. This invariance learning is presented as an alternative to rigid, iterative computations.
Hierarchical hidden Markov models and temporal abstraction
The model extends these ideas into a framework of Hierarchical Hidden Markov Models (HHMMs). Each level of the hierarchy acts as an automaton, with states representing abstractions. Transitions within a state can either terminate or trigger a 'procedure call' to an automaton at a lower level. This structure allows for temporal abstraction, where higher levels operate at a much lower temporal resolution than lower levels. While lower levels might process information at every 'clock tick' (e.g., individual frames in a video), higher levels might sample much less frequently. This hierarchical temporal resolution is crucial for capturing long-range dependencies and complex event sequences over extended periods, essential for tasks like video classification. The entire structure is cast as a graphical model where variables at each level represent states and arcs represent dependencies.
Implementing the model: subnets, distribution, and prior bias
The hierarchical model is decomposed into smaller, manageable components called subnets, designed to be processed on individual processors or cores. These subnets communicate by sharing variables, enabling a distributed message-passing architecture. The model learns not only the conditional probabilities but also the structure of dependencies using methods like Structural EM. To ensure the emergence of slow features, a specific prior bias is applied: the diagonal of the transition matrix within subnets is 'fattened' by increasing diagonal pseudo-counts. This encourages self-transitions, reinforcing the persistence of features over time and discouraging rapid state changes. This bias is a key mechanism for enforcing the 'invariance' property that the model seeks to learn.
From abstract concepts to practical implementation and future directions
The research has progressed to the point of developing MATLAB code for single-process execution on datasets like NIST digits, and an MPI prototype for distributed algorithms. The MATLAB code has been translated to C++ and is being integrated with filters for initial layers that provide invariance to illumination and contrast, using wavelet filters. The team is actively developing the distributed system components and seeking collaborations, particularly within Google's machine vision community. They hope to present working examples in the near future. The core design elements—Neocognitron-inspired hierarchy, slow feature analysis for invariance, Ullman-Sulliv's overlapping receptive fields for consistency, and Bayesian inference frameworks—are presented as translatable engineering principles.
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Common Questions
The research aims to present a mathematical model of the neocortex, focusing on learning invariant features using inertial priors through graphical models and Bayesian inference.
Topics
Mentioned in this video
Associated with Numenta and is using the NIST dataset for their work.
Developed the 'Bayes Net Toolbox', which has been a great contribution to the community and helpful for the project.
Would thoroughly embrace a model that is temporal and predictive.
Co-author of a paper suggesting the casting of visual pathways in terms of Bayesian inference.
Associated with Numenta.
Introduced the terminology of simple and complex cells in the 1960s.
Described a phenomenon in his 1991 PhD thesis where perception involves slowing things down to find stable representations.
Has been working on casting visual pathways in terms of Bayesian inference since the early 90s.
Mathematical representations used to model the cortex, consisting of random variables and arcs representing dependencies.
A framework that shares the idea of finding representations that persist and are stable across space and time.
A core framework used in understanding the hierarchical models of the cortex and propagating information through them.
A method used to learn the structure of graphical models, including which variables depend on others.
An obvious model to use for analyzing the dynamics of patterns over time and reducing high-dimensional input spaces to lower-dimensional ones.
A proposed model for the cortex that is factored into components, where each component is learned by a subnet.
A code developed by Kevin Murphy that has been enormously helpful for the project.
The company is interested in image understanding for tasks like pornography detection and spam filtering, and might be interested in investing in neocortex technology.
The speaker mentions the company in the context of their GPUs being helpful for distributed algorithms, but notes it's not the primary focus of the talk.
A company focused on building neocortexes, which is using the speaker's dataset.
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