Generative Model Out-of-Distribution Detection via Intrinsic Dimensionality
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Solution Overview
Problem
Deep generative models often assign higher likelihood values to out-of-distribution samples than in-distribution samples, leading to unreliable out-of-distribution detection, as they fail to generate data points from regions with high probability densities but low intrinsic dimensionality, making them ineffective for validating data for further automated computer models.
Innovation Solution
The solution involves evaluating both the likelihood and local intrinsic dimensionality of a data sample using a generative model, where a data sample is considered in-distribution if it has a high likelihood and high local intrinsic dimensionality similar to the training data, with calibrated thresholds for likelihood and dimensionality estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep generative models are used to evaluate probability density for out-of-distribution detection, then the model can generate data points consistent with the training distribution, but the model assigns higher likelihood values to out-of-distribution samples than in-distribution samples
Solution Approach 1:
The patent introduces a new dimension of evaluation beyond simple likelihood scoring by estimating the local intrinsic dimensionality of the data manifold. This additional dimensional assessment allows the system to distinguish between high-likelihood in-distribution samples and high-likelihood out-of-distribution samples that lie on lower-dimensional manifolds, thereby resolving the contradiction in likelihood evaluation accuracy.
Solution Approach 2:
The patent changes the evaluation parameters from solely relying on probability density/likelihood to incorporating local intrinsic dimensionality estimates. By transforming the evaluation criteria to include dimensional characteristics of the data manifold, the system achieves more reliable out-of-distribution detection while maintaining accurate likelihood evaluation for in-distribution samples.
2Ease of operation
If the generative model assigns high likelihood to out-of-distribution samples, then the model appears to evaluate these samples as probable, but the model does not generate data points from these high likelihood regions
Solution Approach 1:
The patent implements a feedback mechanism where the local intrinsic dimensionality estimate provides corrective information about the reliability of high likelihood assignments. When a sample shows high likelihood but low local intrinsic dimensionality, the system flags this as inconsistent with proper data generation, thereby resolving the contradiction between evaluation simplicity and generation consistency.
3Device complexity
If only likelihood-based evaluation is used for in-distribution detection, then the evaluation process is simple, but the detection is unreliable due to sharply peaked probability distributions in out-of-distribution regions
Solution Approach 1:
The patent adds the dimension of local intrinsic dimensionality estimation to the evaluation process. This additional metric allows the system to penetrate through the sharply peaked probability distributions that fool likelihood-based methods, providing reliable in-distribution detection while maintaining reasonable process complexity through efficient manifold analysis techniques.
Data Source
AI summary
Generative models are used to determine whether a data sample is in-distribution or out-of-distribution with respect to a training data set. To address potential errors in generative models that attribute high likelihoods to known out-of-distribution data samples, in addition to the likelihood for a data sample, the local intrinsic dimensionality is also evaluated for the data sample. A data sample is determined to belong to the distribution of the training data when the data sample both has sufficient likelihood and local intrinsic dimensionality around its region in the generative model. Different actions may then be determined for the data sample with respect to a data application model based on whether the data sample is in- or out-of-distribution.