Neural Network Model Alignment for Adversarial Robustness
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Solution Overview
Problem
Current path finding algorithms for deep neural networks do not effectively consider the influence of symmetry in the loss surface created by model weight permutations, leading to inefficiencies in finding robust and accurate models, particularly in adversarial environments.
Innovation Solution
The method involves neuron alignment to obtain aligned neural network models, training a minimal loss curve between them, and selecting a new model that maximizes accuracy on adversarially perturbed data, leveraging mode connectivity to enhance robustness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current path finding algorithms are used without considering weight permutations, then the algorithm simplicity is maintained, but the model robustness and accuracy deteriorate
Solution Approach 1:
The patent applies preliminary action by performing neuron alignment before path finding. The alignment step pre-processes the models by reordering neurons to maximize similarity, which simplifies the subsequent path finding process while improving robustness. This preliminary transformation reduces the complexity of finding optimal paths through weight permutations.
Solution Approach 2:
The patent introduces asymmetry by breaking the symmetry of weight permutations through neuron alignment. Instead of treating all neuron orderings equally, the alignment process creates a preferred ordering that maximizes similarity between models, thereby improving robustness without exhaustively searching all symmetric permutations.
2Reliability
If exhaustive search of weight permutations is performed, then model robustness is improved, but computational cost increases
Solution Approach 1:
Neuron alignment serves as a preliminary action that prepares models for efficient path finding. By pre-aligning neurons before searching for paths, the method avoids the need to exhaustively evaluate all weight permutations during the path finding process, significantly reducing computational cost while maintaining robustness.
Solution Approach 2:
The patent changes the parameter representation by transforming models into an aligned coordinate system. This parameter transformation allows the path finding algorithm to operate in a simplified space where robustness can be improved without requiring exhaustive search of all original weight permutation possibilities.
3Productivity
If neuron alignment is performed before path finding, then the efficiency of finding robust models is improved, but the processing time for alignment increases
Solution Approach 1:
The patent applies partial action by performing neuron alignment only on the necessary components and to the necessary degree. Rather than fully optimizing all aspects of model alignment, the method performs sufficient alignment to enable efficient path finding, balancing the time investment in alignment against the gains in search efficiency.
Data Source
AI summary
With at least one hardware processor, obtain data specifying: two trained neural network models; and alignment data. With the at least one hardware processor, carry out neuron alignment on the two trained neural network models using the alignment data to obtain two aligned models. With the at least one hardware processor, train a minimal loss curve between the two aligned models. With the at least one hardware processor, select a new model along the minimal loss curve that maximizes accuracy on adversarially perturbed data.