The fundamentals of cost-effective AI development  

Jun 21, 2023

In this article we will pick up what we have learnt about the concept of robustness and its advantages. We will learn more about how robustness can contribute to more efficient and cost-effective AI development and how it can be implemented in neural network training – during and after the training process.  

Methods for Implementing Robustness During Training 

There are several methods for implementing robustness during neural network training. One of the most common approaches is data augmentation. Data augmentation involves adding synthetic examples to the training data, such as rotating or flipping images or adding noise to audio signals. This can help the network learn to recognize variations in the data, and improve its ability to generalize to new examples. 

Another approach is adversarial training. Adversarial training involves adding adversarial examples to the training data, which are designed to fool the network. By training on these examples, the network learns to recognize and resist adversarial attacks. However, this technique can be computationally expensive and may require a large amount of labeled data. 

Dropout and regularization are other techniques for improving robustness during training. Dropout randomly drops out neurons during training, which helps prevent overfitting and improves generalization. Regularization adds a penalty term to the loss function, which encourages the network to learn simpler and more robust representations. 

Finally, ensemble methods can also improve robustness. Ensemble methods involve training multiple networks and combining their outputs to make predictions. This can improve robustness by reducing the impact of individual network errors and improving generalization. 

Methods for Implementing Robustness After Training 

Even after a network has been trained, there are still ways to improve its robustness. One approach is adversarial training on pre-trained models. This involves generating adversarial examples from the pre-trained model and retraining the network on these examples. This can help the network learn to recognize and resist adversarial attacks without requiring additional labeled data. 

Another approach is fine-tuning with adversarial examples. Fine-tuning involves taking a pre-trained network and retraining it on a smaller set of labeled data. By fine-tuning on adversarial examples, the network can learn to recognize and resist these attacks more effectively. 

Post-processing techniques can also improve robustness after training. This can be achieved with input preprocessing, which involves applying transformations to the input data before it is fed into the network. These transformations can help make the data more robust to variations and noise. Another option is output post-processing, which involves modifying the network’s outputs to make them more robust to errors and uncertainty. 

Spiki’s mission: robust AI to save you time and money 

Implementing robustness during neural network training can be more cost-effective than checking robustness a posteriori for a number of reasons. One advantage of implementing robustness during training is that it can lead to more efficient use of resources. By building a robust AI system from the start, developers can potentially save significant amounts of time, money, and compute resources that would otherwise be spent on post-hoc testing and retraining. This is because building a robust system from the ground up can help ensure that the system performs well under a wide range of conditions, which reduces the likelihood that it will need to be retrained or modified at a later stage. 

Another advantage of implementing robustness during training is that it can lead to more accurate models overall. When robustness techniques are built into the training process, they can help the network learn to generalize better and make more accurate predictions on new, unseen data. This is because robustness techniques like regularization and data augmentation can help prevent overfitting, which is when a model performs well on training data but poorly on new data. By reducing overfitting, robustness techniques can help ensure that a model’s performance is more representative of its true ability to generalize. 

From a client perspective, there are several advantages to using AI systems that have been trained with robustness techniques. For one, a robust system is likely to perform better on new, unseen data, which can lead to more accurate predictions and better decision-making. This is particularly important in high-stakes applications like mobility, healthcare or finance, where accuracy can have a significant impact on outcomes. Additionally, using a robust system can potentially save clients time and money in the long run by reducing the need for retraining or modification down the line. A robust system is less likely to need to be updated or tweaked as data distributions change or new use cases emerge. 

Overall, implementing robustness during neural network training can be a cost-effective way to build accurate, reliable AI systems that are better able to handle unexpected inputs and resist adversarial attacks. By building robustness techniques into the training process, developers can potentially save time, money, and compute resources while also improving the accuracy and generalization of their models. From a client perspective, using a robust AI system can lead to more accurate predictions, better decision-making, and potentially significant cost savings over time. 

Robust neural network training: rely on Spiki 

Rely on Spiki to provide you with robust neural network training fit for your purpose and tailored for your needs. We have developed a unique approach to limit the amount of data needed for our neural network training and either source the data ourselves, or help you take measurements and samples in a predefined and clearly specified manner. Thus we can considerably limit time and efforts needed from your side. Our clients get a fully trained neural network model, which can be deployed via microcontroller, FPGA, as a cloud service or even ASIC as a future step in our product development. 


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