OOD Data Detection via Singular Vector Comparison
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Solution Overview
Problem
Current methods for detecting unknown-class (out-of-distribution) data in machine learning models are inefficient, particularly for large-scale datasets, as they often require hyper-parameter tuning and rely on reconstruction or generation, which degrades performance in real-time applications like healthcare and autonomous driving.
Innovation Solution
A method involving pre-training a dataset with adversarial perturbations, calculating singular vectors using singular value decomposition, and comparing data points to these vectors to determine uncertainty scores, allowing for real-time identification and categorization of in-distribution and out-of-distribution data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If generative models and auto-encoders are used for OOD detection, then OOD detection capability is improved, but hyper-parameter tuning is required and performance degrades on large-scale datasets
Solution Approach 1:
The patent extracts the essential characteristic of OOD data by computing singular vectors that represent the dominant direction of in-distribution data. Instead of using complex generative models, it isolates the key directional information through singular value decomposition, eliminating the need for hyper-parameter tuning while maintaining OOD detection capability.
Solution Approach 2:
The patent replaces the mechanical process of hyper-parameter tuning in generative models with a mathematical computation approach using singular value decomposition. This substitution transforms the iterative, parameter-dependent process into a direct computational method that automatically determines the singular vectors without requiring manual parameter adjustment.
2Reliability
If reconstruction or generation methods are used for OOD detection, then OOD detection is enabled, but performance degrades on large-scale datasets and video classification
Solution Approach 1:
The patent extracts only the essential directional information (singular vectors) from the data without requiring full reconstruction or generation. This extraction approach significantly reduces computational overhead while preserving the ability to detect OOD samples, enabling efficient processing of large-scale datasets and video classification tasks.
Solution Approach 2:
Instead of using reconstruction or generation to detect OOD samples, the patent inverts the approach by directly comparing singular vectors of test samples with the pre-computed singular vectors of in-distribution data. This inversion eliminates the computationally expensive reconstruction step while maintaining detection accuracy.
3Reliability
If currently known OOD detection techniques are used, then OOD detection is achieved, but real-time detection efficiency is reduced
Solution Approach 1:
The patent performs preliminary computation of singular vectors from the training data and stores them for future use. During real-time detection, only a simple comparison between test sample singular vectors and the pre-computed reference vectors is needed, dramatically reducing detection time while maintaining accuracy.
Solution Approach 2:
The patent creates a simplified copy of the essential data characteristics in the form of singular vectors, which can be rapidly compared against test samples. This copying approach avoids the need for complex real-time analysis while preserving the ability to accurately identify OOD samples.
Data Source
AI summary
Described, herein, relates to a system of and method for digitally monitoring a large-scale dataset on a computing device and automatically detecting, in real-time, unknown class data in order to aid a machine learning model. Once machine learning models are deployed in the real-world applications, the models tend to encounter unknown-class (i.e., out-of-distribution) (hereinafter “OOD”) data during inference. Detecting out-of-distribution data is a crucial task in safety-critical applications to ensure safe deployment of deep learning models. It is desired that the machine learning model should only be confident about the type of data that has already seen in-distribution (hereinafter “ID”) class data which reinforces the driving principle of the OOD detection. The system and method may rely on contrastive feature learning of the largescale datasets, where the embeddings lie on a compact low-dimensional space. Additionally self-supervised fine-tuning may then be performed by mapping an ID class feature into uni-dimensional sub-space.