Neural Network Confidence Estimation for Reliable Pixel Classification

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Solution Overview

Problem

Classification systems in images often provide classifications with low confidence values that can lead to errors, affecting systems that rely on these classifications, particularly in autonomous vehicles, medical imaging, geosensing, and agriculture environments.

Innovation Solution

A system for segmentation and confidence value determination using neural networks that assign confidence values to pixel classifications, comprising a neural network, score distribution transformation, score normalization, and confidence determination, to enhance the reliability of image classification.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a neural network provides classification with high confidence, then the system can make decisive operations, but errors may occur when confidence is misleadingly high

Engineering Contradiction:

Improveclassification reliabilityVSAvoidconfidence accuracy

Core Design Contradiction:

ReliabilityVSMeasurement precision

Solution Approach 1:

The patent introduces an intermediary confidence estimation mechanism that mediates between the neural network's raw output and the final classification decision. This intermediary layer provides a more accurate confidence assessment by considering multiple factors beyond simple probability outputs, thereby resolving the contradiction between decisive action and error prevention

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system implements feedback mechanisms where confidence values are continuously refined based on performance metrics and error patterns. The confidence estimation adapts over time by incorporating feedback from actual classification outcomes, improving both reliability and confidence accuracy through iterative learning

Inventive Principle:

Principle #23Feedback

2Productivity

If the system performs operations based on provided classifications, then productivity increases, but errors adversely affect system performance

Engineering Contradiction:

Improveoperational efficiencyVSAvoidclassification accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent applies dynamic adjustment of operational thresholds based on confidence levels. When confidence is high, the system operates autonomously to maintain productivity; when confidence is low or uncertain, the system dynamically adjusts by seeking additional verification or human review, thereby maintaining both productivity and reliability

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system prepares compensatory measures in advance by establishing fallback procedures and verification mechanisms for low-confidence classifications. This prior cushioning ensures that when errors occur, the system can recover without significant impact on overall productivity

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

Data Source

PatentUS20260003936A1Confidence generation using a neural network

Publication Date: 2026.01.01 NVIDIA CORP

AI summary

Apparatuses, systems, and techniques to generate one or more confidence values associated with one or more objects identified by one or more neural networks. In at least one embodiment, one or more confidence values associated with one or more objects identified by one or more neural networks are generated based on, for example, one or more neural network outputs.