Neural Network OOD Embedding Distance Optimization
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Solution Overview
Problem
Existing machine learning models trained in closed-set settings struggle to reliably distinguish between in-distribution and out-of-distribution data, often producing highly confident but unreliable predictions for out-of-distribution samples, which is critical for safety-critical applications like autonomous driving.
Innovation Solution
A computer-implemented method for training a neural network system that involves obtaining out-of-distribution embeddings, determining a loss function based on similarity or distance to prototypes of in-distribution embeddings, and modifying system parameters to reduce this loss, effectively pushing out-of-distribution embeddings away from training classes, enhancing the model's ability to differentiate between in-distribution and out-of-distribution data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cross-entropy loss is used to train classification models, then the model achieves high confidence predictions for training classes, but it produces highly confident but unreliable predictions for out-of-distribution data
Solution Approach 1:
The patent segments the prediction space by introducing a separate OOD detection mechanism alongside the classification head. The model is divided into distinct functional components: one for in-distribution classification and another for OOD detection, allowing independent optimization of each function without interference
Solution Approach 2:
The patent introduces an intermediary OOD embedding layer that transforms input features into a separate OOD representation space. This intermediary layer acts as a mediator between the main classification network and the OOD detection loss, enabling the model to detect out-of-distribution samples without affecting the primary classification confidence
2Manufacturing precision
If the model is trained to always select an object class with confidence close to 1, then it achieves high accuracy on training data, but it cannot detect out-of-distribution samples
Solution Approach 1:
The patent implements multi-functionality by designing a single neural network architecture that simultaneously performs classification and OOD detection. The shared backbone features are used for both in-distribution classification and out-of-distribution detection through separate loss functions, making the model versatile without requiring separate specialized systems
Solution Approach 2:
The patent applies preliminary action by pre-training the model on in-distribution data to achieve high classification accuracy, then fine-tuning with an additional OOD detection loss function. This preliminary classification training establishes a strong base that is subsequently enhanced with OOD detection capabilities through incremental learning
3Productivity
If cross-entropy loss is used for training, then the model converges quickly on training data, but it becomes sensitive to noise and susceptible to overfitting
Solution Approach 1:
The patent merges multiple loss functions (cross-entropy loss for classification and OOD detection loss for robustness) into a unified training objective. This combination allows the model to simultaneously optimize for fast convergence on training data while maintaining robustness against noise and overfitting through the complementary nature of the combined losses
Data Source
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AI summary
A computer-implemented method for training a neural network system, comprising: obtaining a first out-of-distribution, OOD, embedding of an OOD input sample that is out of each class in a training distribution comprising one or more classes; determining a value of a first loss function based on similarity or a distance between the first OOD embedding and one of both a prototype of a class in the training distribution and an in-distribution ("ID") embedding of an ID input sample belonging to the one or more classes, wherein the value of the first loss function positively depends on the similarity or negatively depends on the distance; and modifying a parameter of the system to reduce the value of the first loss function