Adversarial Training Caching for Classifier Robustness
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Solution Overview
Problem
Current adversarial training methods for classifiers, such as neural networks, are computationally expensive and time-consuming, making it challenging to deploy robust classifiers in real-world applications like semi-autonomous robots, as they require significant resources to generate adversarial examples effectively.
Innovation Solution
The method involves caching intermediate perturbations for each input signal and using them as starting points for the next training epoch, combined with Optimistic ADAM updates and a learning rate schedule that increases and then decreases, significantly speeding up the adversarial training process with minimal loss of robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard adversarial training methods are used to train classifiers, then robustness against adversarial examples is improved, but training time and computational cost increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-computing and caching adversarial perturbations before the main training process. The method generates adversarial examples in advance using a first model, caches them in a database, and then uses these pre-computed adversarial examples during training with a second model. This eliminates the need to re-generate adversarial perturbations during each training epoch, significantly reducing training time while maintaining robustness.
Solution Approach 2:
The patent uses copying by creating and storing copies of adversarial perturbations in a cache database. Instead of regenerating the same adversarial examples repeatedly during training, the system copies them once and reuses them across multiple training epochs. This copying mechanism reduces computational overhead while preserving the robustness benefits of adversarial training.
2Reliability
If adversarial training is performed without caching perturbations, then robustness is maintained, but computational resources are wasted by regenerating the same perturbations repeatedly
Solution Approach 1:
The patent implements copying by generating adversarial perturbations once and creating cached copies for reuse. The system stores these perturbations in a database and retrieves them during training instead of regenerating them. This eliminates redundant computation while maintaining the same robustness效果, thereby reducing energy and computational resource consumption.
Solution Approach 2:
The patent applies parameter changes by modifying the training process to use pre-computed perturbations with fixed parameters. Instead of dynamically generating new perturbations with varying parameters during each training step, the system uses cached perturbations with stable parameters, reducing the computational complexity and resource usage associated with perturbation generation.
3Reliability
If multiple epochs of adversarial training are run to ensure robustness, then classifier reliability improves, but training duration extends to hours or days
Solution Approach 1:
The patent applies preliminary action by pre-generating adversarial perturbations before the multi-epoch training process. This allows the training to proceed efficiently through multiple epochs using the same cached perturbations without the overhead of regenerating them each time, thereby maintaining robustness while improving training speed and productivity.
Solution Approach 2:
The patent implements continuity of useful action by maintaining a persistent cache of adversarial perturbations that can be continuously reused across multiple training epochs. This eliminates interruptions and redundant computations, allowing the training process to continue efficiently over multiple epochs without the computational burden of regenerating perturbations, thus improving training speed while maintaining robustness.
Data Source
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AI summary
A computer-implemented method for training a classifier (60) for classifying input signals (x) obtained from a sensor (30), comprising the steps of: - accessing, from a memory (146), the classifier (60), including providing initial values of parameters (φ) characterizing said classifier (60); - providing a set of training data comprising pairs of training input signals (xi) and corresponding training output signals (yi); - initializing a set of perturbations (δi) corresponding to each one of said training input signals (xi); - performing a first plurality of training epochs, each epoch comprising a second plurality (N) of training steps for each input signal (xi), each of said training steps comprising - providing one of said input signals (xi); - providing an adversarial input signal xiadv depending on said input signal (xi) and the perturbation (δi) that corresponds to said provided input signal (xi); - updating the perturbation (δi) based on its value from the previous epoch and a value that characterizes a gradient with respect to said perturbation (δi) to a loss function (ℓ) which characterizes a difference between the output signal provided by said classifier (60) if inputted said adversarial input signal xiadv and the training output signal (yi); - freezing the value of said updated perturbation (δi) until its update in the next epoch; - updating said parameters (φ) depending on a gradient with respect to said parameters (φ) of said loss function (ℓ).