Robert bosch gmbh (20240256889). METHOD AND APPARATUS FOR DEEP LEARNING simplified abstract
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METHOD AND APPARATUS FOR DEEP LEARNING
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METHOD AND APPARATUS FOR DEEP LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240256889 titled 'METHOD AND APPARATUS FOR DEEP LEARNING
The abstract describes a method for deep learning that involves receiving a plurality of samples and labels, adversarially augmenting the samples based on a threat model, and assigning low predictive confidence to adversarially augmented samples with noisy labels.
- The method involves a deep learning model receiving samples and labels.
- The samples are adversarially augmented based on a threat model.
- Low predictive confidence is assigned to adversarially augmented samples with noisy labels.
Potential Applications: - Enhancing the robustness of deep learning models in the face of adversarial attacks. - Improving the accuracy of deep learning models in scenarios with noisy labels.
Problems Solved: - Addressing the vulnerability of deep learning models to adversarial attacks. - Mitigating the impact of noisy labels on the performance of deep learning models.
Benefits: - Increased resilience of deep learning models against adversarial attacks. - Enhanced accuracy and reliability of deep learning models in real-world applications.
Commercial Applications: Title: "Enhancing Deep Learning Model Robustness for Secure Applications" This technology could be applied in industries such as cybersecurity, autonomous vehicles, healthcare diagnostics, and financial fraud detection. The market implications include improved security and reliability in critical systems.
Prior Art: Further research can be conducted in the areas of adversarial machine learning, robust deep learning, and label noise mitigation techniques to explore existing solutions and advancements in the field.
Frequently Updated Research: Stay updated on the latest developments in adversarial machine learning, robust deep learning, and label noise mitigation techniques to leverage cutting-edge advancements in the field.
Questions about Deep Learning Model Robustness: 1. How does adversarial augmentation improve the robustness of deep learning models?
- Adversarial augmentation introduces perturbations to samples, making the model more resilient to adversarial attacks by learning from these perturbed examples.
2. What are the implications of assigning low predictive confidence to adversarially augmented samples with noisy labels?
- This helps the model distinguish between reliable and unreliable samples, improving overall performance and accuracy.
Original Abstract Submitted
a method for deep learning. the method includes: receiving, by a deep learning model, a plurality of samples and a plurality of labels corresponding to the plurality of samples; adversarially augmenting, by the deep learning model, the plurality of samples based on a threat model; and assigning, by the deep learning model, a low predictive confidence to one or more adversarially augmented samples of the plurality of adversarially augmented samples having noisy labels due to the adversarially augmenting based on the threat model.
