18341397. Adaptive Learning Rates for Training Adversarial Models with Improved Computational Efficiency simplified abstract (GOOGLE LLC)

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Adaptive Learning Rates for Training Adversarial Models with Improved Computational Efficiency

Organization Name

GOOGLE LLC

Inventor(s)

Hussein Hazimeh of New York NY (US)

Natalia Borisovna Ponomareva of Hoboken NJ (US)

Adaptive Learning Rates for Training Adversarial Models with Improved Computational Efficiency - A simplified explanation of the abstract

This abstract first appeared for US patent application 18341397 titled 'Adaptive Learning Rates for Training Adversarial Models with Improved Computational Efficiency

Simplified Explanation

The patent application describes systems and methods for dynamically adjusting the learning rate of an adversarial model using a novel scheduling technique. The technique aims to maintain a balance between the adversarial components of the model. Here are the key points:

  • The learning rate of an adversarial model is adjusted dynamically using a unique scheduling technique.
  • The technique is based on the fact that the loss of an ideal adversarial network can be determined in advance in certain scenarios.
  • A scheduler component is employed to ensure that the loss of the optimized network remains close to that of an ideal adversarial network.

Potential Applications

  • This technology can be applied in various fields where adversarial models are used, such as computer vision, natural language processing, and cybersecurity.
  • It can enhance the performance and stability of adversarial models in tasks like image classification, text generation, and anomaly detection.

Problems Solved

  • Adversarial models often struggle to maintain a proper balance between their components, leading to suboptimal performance.
  • Determining an appropriate learning rate for adversarial models can be challenging, as it needs to be adjusted dynamically based on the specific scenario.
  • This technology addresses these issues by providing a novel scheduling technique that adapts the learning rate to maintain the desired balance and improve overall performance.

Benefits

  • The dynamic learning rate scheduling technique ensures that the adversarial model remains optimized and performs closer to an ideal adversarial network.
  • By maintaining a proper balance between adversarial components, the model can achieve better accuracy and stability.
  • The technique simplifies the process of determining an appropriate learning rate for adversarial models, making them more accessible and easier to implement.


Original Abstract Submitted

Provided are systems and methods that use a novel learning rate scheduling technique to dynamically adapt the learning rate of an adversarial model to maintain an appropriate balance between adversarial components of the model. The scheduling technique is driven by the fact that, in some settings, the loss of an ideal adversarial network can be analytically determined a priori. A scheduler component can thus operate to keep the loss of the optimized network close to that of an ideal adversarial net.

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