Neural Network Configuration for Equivariant Learning With Less Training Data
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Solution Overview
Problem
The high cost and inefficiency of collecting and labeling training data for neural networks in semi-automated driving systems, particularly in scenarios requiring generalization to unseen situations, highlight the need for a more effective method to incorporate prior knowledge and reduce data collection efforts.
Innovation Solution
A method is developed to configure neural networks to exhibit invariant or equivariant behavior by specifying transformations and setting up equations that link these conditions to the network's architecture, allowing prior knowledge about symmetries to be directly incorporated, thereby reducing the need for extensive training data and enhancing data efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive training data is collected and manually labeled for neural network training, then the neural network can learn to generalize to unseen situations, but the cost and time required for data collection and labeling increase significantly
Solution Approach 1:
The patent applies preliminary action by configuring the neural network architecture to exhibit equivariant or invariant behavior before training begins. By incorporating symmetry knowledge into the network structure in advance, the system reduces the amount of training data needed to achieve generalization, thereby reducing data collection and labeling time while maintaining reliability.
Solution Approach 2:
The patent uses synthetic training data with known setpoint outputs as copies of real-world scenarios. This allows the neural network to learn from simulated examples that preserve the essential characteristics of real data without requiring extensive manual collection and labeling of actual driving scenarios.
2Loss of time
If synthetic training data with known setpoint outputs is used, then data collection time is reduced, but the quality and realism of training data may be compromised
Solution Approach 1:
The patent combines preliminary configuration of equivariant/invariant architecture with synthetic data generation. The network is pre-configured with symmetry properties, and synthetic data is generated that respects these properties, ensuring both time efficiency and training quality without requiring extensive manual data collection.
3Adaptability or versatility
If the neural network architecture is configured to exhibit equivariant or invariant behavior through mathematical equations, then prior knowledge about symmetries is incorporated directly, but the complexity of network configuration increases
Solution Approach 1:
The patent implements parameter changes by modifying the neural network's architectural parameters (weights and structure) to satisfy mathematical equations that enforce equivariant or invariant behavior. This allows the network to adapt to transformations while maintaining a systematic configuration approach rather than increasing overall complexity.
Solution Approach 2:
The patent introduces mathematical equations as intermediaries that link the desired equivariant or invariant behavior to the neural network architecture. These equations serve as a bridge between the symmetry requirements and the network configuration, systematically translating abstract symmetry concepts into concrete architectural constraints.
4Measurement precision
If manual labeling of training data is performed, then accurate setpoint outputs are obtained, but the cost and effort required increase significantly
Solution Approach 1:
The patent uses synthetic training data with known setpoint outputs as substitutes for manually labeled real-world data. This copying approach maintains measurement precision by ensuring accurate ground truth labels are available in the synthetic data, while dramatically reducing the manual labeling effort required.
Data Source
AI summary
A method for configuring a neural network which is designed to map measured data to one or more output variables. The method includes: transformation(s) of the measured data is/are specified which when applied to the measured data, is/are meant to induce the output variables supplied by the neural network to exhibit an invariant or equivariant behavior; at least one equation is set up which links a condition that the desired invariance or equivariance be given with the architecture of the neural network; by solving the at least one equation a feature is obtained that characterizes the desired architecture and/or a distribution of weights of the neural network in at least one location of this architecture; a neural network is configured in such a way that its architecture and/or its distribution of weights in at least one location of this architecture has/have all of the features ascertained in this way.