Generator Neural Network Training With Fidelity-Based Equivariance Constraints
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Solution Overview
Problem
Existing methods for training generator neural networks lack robustness and flexibility in incorporating data symmetries, leading to the generation of unrealistic or physically impossible data, especially in applications like autonomous driving where safety-critical decisions are made.
Innovation Solution
Incorporate fidelity-preserving and fidelity-destroying transformations into the training process of generator neural networks, using them as additional constraints in the loss function to ensure the generated data maintains the desired symmetries and is distinguishable from real data, thereby improving the network's equivariance and robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generator neural networks are trained using standard adversarial methods, then the network can generate synthesized sensor data, but the generated data may be unrealistic or physically impossible due to lack of equivariance constraints
Solution Approach 1:
The patent applies preliminary action by incorporating equivariance constraints into the training process before the generator produces final output. Fidality-preserving transformations are applied during training to pre-establish the equivariance properties that the generator must learn, ensuring that the network understands symmetry relationships before generating data. This prevents the generation of physically impossible data by establishing correct transformation rules in advance.
Solution Approach 2:
The patent implements feedback mechanisms through the discriminator network that provides signals to the generator about whether generated data maintains proper equivariance relationships. The discriminator is trained to distinguish between real and synthesized data while considering transformation consistency, and this feedback loop guides the generator to improve its equivariance properties iteratively, enhancing the realism of generated data.
2Reliability
If fidelity-preserving transformations are applied during training, then the generator learns equivariance and produces more realistic data, but the training process becomes more complex and computationally intensive
Solution Approach 1:
The patent applies universality by designing a unified training framework that handles both standard adversarial training and equivariance-constrained training through a single discriminator network. The discriminator is configured to evaluate both the authenticity of generated data and its transformation consistency, allowing one component to serve multiple functions. This reduces overall system complexity compared to using separate networks for different training objectives.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting the weighting of equivariance constraints during training. The loss function incorporates transformable data with adjustable weights that can be modified throughout training to balance between generating realistic data and maintaining equivariance properties. This allows the training process to adapt to different stages of learning without requiring complete redesign of the training architecture.
3Manufacturing precision
If the generator is optimized to produce indistinguishable synthesized data, then the discriminator becomes harder to train, but the generated data quality improves
Solution Approach 1:
The patent applies asymmetry by introducing transformation-specific evaluation criteria that create asymmetric training pressures on the discriminator. Instead of treating all generated data equally, the discriminator is trained to evaluate data under different transformation conditions (e.g., rotated, flipped, scaled versions), creating asymmetric challenges that prevent the generator from exploiting simple symmetries. This maintains discriminator trainability while improving synthesized data quality through equivariance enforcement.
Data Source
AI summary
A training method for training a generator neural network configured to generate synthesized sensor data. A fidelity destroying transformation is defined configured to transform a measured sensor data to obtain a fidelity-destroyed transformed measured sensor data. A fidelity preserving transformation is defined configured to transform a measured sensor data to obtain a fidelity-preserved transformed measured sensor data.