Reciprocal Generative Models for Out-of-Distribution Detection
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Solution Overview
Problem
Deep generative models face challenges in accurately identifying out-of-distribution samples, leading to increased likelihood of misclassifying sensor data, which hinders their use as effective classifiers.
Innovation Solution
Training a set of reciprocal generative models, where each model is configured to minimize loss on in-distribution samples and maximize loss on both sampled and generated out-of-distribution samples, to differentiate between classes and reject out-of-distribution data effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If deep generative models are used for classification tasks, then the model can fit examples into each class separately with a probability distribution, but the model fails to accurately identify out-of-distribution samples leading to increased misclassification
Solution Approach 1:
The patent divides the classification task into K separate generative models, each responsible for a specific class. Each model independently learns to generate samples from its designated class while explicitly training to reject out-of-distribution samples. This segmentation allows each model to specialize in its class while maintaining the ability to identify and reject samples from other classes or unknown distributions.
Solution Approach 2:
The patent implements a feedback mechanism where each generative model is trained on both in-distribution samples (from its target class) and out-of-distribution samples (from other classes). The model receives feedback in the form of loss signals that encourage it to minimize loss on in-distribution samples while maximizing loss on out-of-distribution samples. This feedback loop continuously refines the model's ability to distinguish between valid and invalid inputs.
2Measurement precision
If generative models are trained to maximize likelihood of in-distribution samples, then the model can effectively classify samples belonging to its class, but the model cannot reliably reject out-of-distribution samples
Solution Approach 1:
The patent applies local quality by training each generative model to have different objectives for different types of inputs. For in-distribution samples from its target class, the model minimizes loss to maximize likelihood. For out-of-distribution samples from other classes, the model maximizes loss to minimize likelihood. This differentiated objective function allows the model to optimize its behavior locally for different input types simultaneously.
Solution Approach 2:
The patent inverts the traditional training approach by explicitly teaching the model to reject out-of-distribution samples rather than only teaching it to accept in-distribution samples. Instead of merely maximizing likelihood on valid samples, the model is also trained to maximize loss on invalid samples. This inversion of the training objective enables the model to develop strong rejection capabilities alongside its classification functionality.
Data Source
AI summary
For each generative model of a set of K generative models that classifies sensor data into K classes, in-distribution samples are sampled from training data as being classified as belonging to the class of the generative model and out-of-distribution samples are sampled from the training data as being classified as not belonging to the class of the generative model. Out-of-distribution samples are also generated from each remaining reciprocal generative model in the set of reciprocating generative models excluding the generative model to provide additional samples classified as not belonging to the class of the generative model. Parameters of the generative model are updated to minimize a loss function to maximize likelihood of the samples belonging to the class, and to maximize the loss function on both the sampled out-of-distribution samples and the generated out-of-distribution samples to minimize likelihood of the samples not belonging to the class.