Parameterized Label Smoothing for Robust ML Training
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Solution Overview
Problem
Existing machine learning models are vulnerable to adversarial input perturbations, leading to poor scalability and limited robustness against diverse attacks, as standard adversarial training methods require significant computational overhead and often fail to generalize well beyond the specific attacks they were trained on.
Innovation Solution
The self-progressing robust training (SPROUT) method employs parameterized label smoothing, Gaussian augmentation, and mixup techniques to enhance model robustness, alleviating the need for attack generation and improving scalability while maintaining or exceeding the robustness of existing methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard adversarial training methods are used to improve model robustness, then model robustness against specific attacks is improved, but computational overhead increases significantly and scalability deteriorates
Solution Approach 1:
The patent extracts and removes the computationally expensive attack generation component from the training process. Instead of generating adversarial examples during training, the method uses only clean training data with modified loss functions (entropy regularization and confidence penalization) to achieve robustness without the overhead of adversarial example generation.
Solution Approach 2:
The patent inverts the traditional adversarial training approach by not training against adversarial examples, but rather training on clean data with regularization terms that penalize over-confidence and encourage entropy. This inverse approach achieves robustness through a fundamentally different mechanism that avoids computational overhead.
2Reliability
If standard adversarial training methods are used to improve model robustness, then resistance to specific attacks is improved, but generalization to diverse attacks deteriorates
Solution Approach 1:
The patent creates a universal training method that works across diverse attack types without requiring attack-specific training. The entropy regularization and confidence penalization mechanisms provide general-purpose robustness that transfers to multiple attack scenarios, making the model adaptable to diverse threats rather than specialized for specific attacks.
Solution Approach 2:
The patent changes the optimization parameters by modifying the loss function to include entropy regularization and confidence penalization terms. This parameter change in the training objective function enables the model to learn robust decision boundaries that generalize across different attack types, rather than overfitting to specific attack patterns.
3Stability of the object's composition
If label smoothing with fixed parameters is used, then training stability is improved, but adaptability to different training stages deteriorates
Solution Approach 1:
The patent makes the label smoothing parameter dynamic by scheduling it to change during training. The parameter α is set to decrease over training epochs, providing strong smoothing early in training for stability and reducing it later to allow the model to learn precise decision boundaries. This dynamic adjustment maintains both stability and adaptability across different training stages.
Solution Approach 2:
The patent implements periodic adjustment of the label smoothing parameter through a scheduled decay mechanism. The parameter is updated at regular intervals during training, transitioning from high smoothing values in early epochs to lower values in later epochs, creating a periodic pattern that balances stability and adaptability throughout the training process.
Data Source
AI summary
Techniques for training robust machine learning models for adversarial input data. Training data for a machine learning (ML) model is received. The training data includes a plurality of labels for data elements. First modified training data is generated by modifying one or more of the plurality of labels in the training data using parameterized label smoothing with a first optimization parameter. The ML model is trained using the first modified training data. The training includes updating a first one or more model weights in the ML model, and generating a second optimization parameter suitable for use in future parameterized label smoothing for future training of the ML model