Generative-Discriminative Feature Representations for Open-Set Image Classification
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Solution Overview
Problem
Conventional computer vision systems face challenges in accurately classifying open-set objects due to limitations in learning descriptive feature spaces, often misclassifying them as known classes, leading to decreased classification accuracy and increased system errors.
Innovation Solution
The use of generative-discriminative feature representations, combining a generative model and self-supervision to learn a richer, deeper feature space, allows for accurate detection of open-set objects by generating reconstructions of known classes and utilizing self-supervision to improve discriminative features, thereby enhancing classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional classification systems use finite labeled training examples from known classes, then the system can perform classification for known classes, but it misclassifies open-set objects as known classes due to limited feature space representation
Solution Approach 1:
The patent extends the traditional feature space by adding a generative dimension. Instead of only discriminative features from labeled data, the system incorporates generative features that model the distribution of known classes. This dimensional extension allows the system to distinguish open-set objects by comparing them against synthesized examples, resolving the contradiction between reliable known-class classification and adaptability to open-set recognition.
Solution Approach 2:
The patent introduces generative models as an intermediary between the input image and the classification decision. The generative model synthesizes training examples that act as a mediator, enabling the classifier to compare actual images against generated representations of known classes. This intermediary layer improves both reliability for known classes and adaptability for open-set detection.
2Productivity
If the system learns a descriptive feature space based on finite labeled training examples, then classification can be performed efficiently, but misclassification errors occur for open-set objects
Solution Approach 1:
The patent applies preliminary action by pre-training generative models on labeled data before the actual classification task. The generative models are trained beforehand to learn the distribution of each class, creating a library of synthesized examples that prepare the system for accurate classification. This preliminary training enables both efficient processing during inference and high precision in distinguishing open-set objects.
Solution Approach 2:
The patent uses copying by generating synthetic training examples through generative models. Instead of relying solely on finite labeled examples, the system creates multiple copies and variations of training data through image synthesis. These generated copies enrich the feature space and improve measurement precision without sacrificing classification efficiency.
3Device complexity
If conventional systems classify all inputs as known classes, then the system operates with simple classification logic, but it produces false-positive classifications for open-set images
Solution Approach 1:
The patent segments the classification process into two distinct stages: a generative stage that synthesizes class-specific examples, and a discriminative classification stage that compares input images against generated representations. This segmentation allows the system to maintain relatively simple classification logic while dramatically reducing false positives through the added generative verification step.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for classifying an input image utilizing a classification model conditioned by a generative model and/or self-supervision. For example, the disclosed systems can utilize a generative model to generate a reconstructed image from an input image to be classified. In turn, the disclosed systems can combine the reconstructed image with the input image itself. Using the combination of the input image and the reconstructed image, the disclosed systems utilize a classification model to determine a classification for the input image. Furthermore, the disclosed systems can employ self-supervised learning to cause the classification model to learn discriminative features for better classifying images of both known classes and open-set categories.