Generative Model Gradient Estimation via Multi-Point Sampling
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Solution Overview
Problem
During the optimization process of a loss function using a generative model, gradients can become too small or null, leading to training instability and mode collapse, which hinders the convergence of the optimization process.
Innovation Solution
A computer-implemented method that modifies the input variable value by generating a plurality of modified input variable values and computing their gradients, combining these gradients to generate an estimated gradient, and updating the input variable value in the direction determined by the estimated gradient, thereby overcoming the issue of vanishing gradients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If standard gradient-based optimization is used during GAN training, then the training process follows a straightforward optimization path, but the gradients become very small or null leading to vanishing generator gradients and training instability
Solution Approach 1:
The patent introduces an intermediary mechanism (gradient estimation through multiple sampling points) between the generator and discriminator to obtain more reliable gradient signals. Instead of directly using the vanishing gradients from standard backpropagation, the method computes gradients at multiple intermediate points in the latent space and combines them to form a more stable estimated gradient that guides generator updates.
Solution Approach 2:
The patent changes the parameter computation approach by evaluating the loss function at multiple different input variable values (sampling points) rather than relying on a single gradient computation. This parameter change transforms the gradient estimation from a single-point derivative to a multi-point numerical approximation, preventing the vanishing gradient problem.
2Measurement precision
If exhaustive search through the high-dimensional latent space is performed to understand generator behavior, then complete coverage of the latent space is achieved, but the analysis becomes intractable due to computational complexity
Solution Approach 1:
The patent applies partial action by selecting and evaluating only specific sampling points in the latent space rather than performing an exhaustive search of all possible points. This selective approach provides sufficient insight into generator behavior and gradient characteristics without the computational burden of complete coverage.
Solution Approach 2:
The patent transforms the problem from analyzing gradients in the original high-dimensional latent space to analyzing gradients along specific trajectories or manifolds within that space. This dimensional reduction through trajectory-based sampling makes the analysis tractable while preserving essential information about generator behavior.
3Manufacturing precision
If the search through latent space is conditioned on classification model response to ensure meaningful instances, then the quality and semantic relevance of generated samples improve, but the optimization becomes more complex requiring additional computations
Solution Approach 1:
The patent implements feedback by using the classification model's response to guide the gradient estimation process. The classification results provide feedback signals that inform which sampling points are most informative for understanding generator behavior, allowing the optimization to focus on regions of the latent space that produce semantically meaningful variations.
Data Source
AI summary
A method to generate synthetic data instances. The method includes generating a synthetic data instance for an input variable value of an input variable supplied to the generative model, classifying the synthetic data instance to generate a classification result, determining a loss function value of a loss function, the loss function evaluating the classification result and determining the gradient of the loss function with respect to the input variable. Depending on the absolute value of the gradient, the method includes generating a plurality of modified input variable values, determining, for each modified input variable value, the gradient of the loss function, combining the gradients of the loss function to generate an estimated gradient, and modifying the input variable value in a direction determined by the estimated gradient to generate a further input variable value. The generative model generates a further synthetic data instance for the further input variable value.