Smooth Activation Functions for Robust Adversarial Training
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Solution Overview
Problem
Existing neural networks are vulnerable to adversarial attacks, and existing defense mechanisms either fail to improve robustness or incur significant computational costs.
Innovation Solution
Implement smooth activation functions with continuous gradients to replace non-smooth activation functions during training, allowing for improved robustness without additional computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If non-smooth activation functions are used in neural networks, then computational simplicity is maintained, but robustness against adversarial attacks deteriorates
Solution Approach 1:
The patent applies parameter changes by modifying the activation function's mathematical properties - specifically transitioning from non-smooth functions like ReLU to smooth functions like Softplus. This parameter change in the activation function's differentiability enables gradient-based adversarial training while maintaining computational efficiency, thus improving robustness without significantly increasing complexity.
2Reliability
If adversarial training is implemented to improve robustness, then model security is enhanced, but training computational cost increases
Solution Approach 1:
The patent uses smooth activation functions as an intermediary that enables efficient gradient computation during adversarial training. The smoothness property allows gradients to flow through the network without discontinuities, facilitating the generation of adversarial examples and the training process while keeping computational costs manageable compared to other robust training methods.
3Reliability
If smooth activation functions are used during training, then robustness is improved, but inference speed may be reduced
Solution Approach 1:
The patent applies preliminary action by performing the computationally intensive smooth activation function computations during the training phase only. Once training is complete and the model is deployed for inference, the weights are fixed and the smooth activation functions are replaced or approximated with faster operations, thus achieving robustness during training without sacrificing inference speed during deployment.
Data Source
AI summary
Generally, the present disclosure is directed to the training of robust neural network models by using smooth activation functions. Systems and methods according to the present disclosure may generate and/or train neural network models with improved robustness without incurring a substantial accuracy penalty and/or increased computational cost, or without any such penalty at all. For instance, in some examples, the accuracy may improve. A smooth activation function may replace an original activation function in a machine-learned model when backpropagating a loss function through the model. Optionally, one activation function may be used in the model at inference time, and a replacement activation function may be used when backpropagating a loss function through the model. The replacement activation function may be used to update learnable parameters of the model and/or to generate adversarial examples for training the model.