Batch Softmax Normalization For 0-Label And Multilabel Classification
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Solution Overview
Problem
Softmax-based confidence scores in deep neural networks are unreliable due to overconfidence on in-distribution data and vulnerability to adversarial attacks, and they fail to effectively handle 0-label and N-label classification problems where the categorical assumption does not hold.
Innovation Solution
The implementation of batch softmax normalization, which generates a global normalization factor based on a batch of logits from a neural network, normalizes logit values, and maps them to manifolds in a coordinate space representing different numbers of labels, allowing for accurate classification beyond single-label problems and reducing ambiguity by using a data-set-dependent normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If softmax normalization is used for single-label classification, then the model achieves categorical probability distribution, but it fails to handle 0-label and N-label classification problems where the categorical assumption does not hold
Solution Approach 1:
The patent segments the traditional softmax normalization into two distinct components: (1) L2 normalization that preserves the directional information of logits, and (2) a temperature scaling parameter that controls confidence calibration. This segmentation allows the model to handle different classification scenarios (single-label, 0-label, N-label) by adjusting the temperature parameter without changing the core normalization mechanism, thereby improving versatility while maintaining reliability through proper calibration.
Solution Approach 2:
The patent introduces a temperature parameter that can be dynamically adjusted to change the behavior of the normalization. By modifying this parameter, the model can adapt to different classification problems: standard softmax behavior for single-label, and calibrated probability distributions for 0-label and N-label problems. This parameter change approach resolves the contradiction by providing a unified framework that reliably handles diverse classification scenarios.
2Measurement precision
If deep neural networks use softmax-based confidence scores, then the model provides probabilistic interpretation, but the scores become overconfident on in-distribution data and out-of-distribution data
Solution Approach 1:
The patent implements temperature scaling as a feedback mechanism that calibrates confidence scores based on the relationship between predicted probabilities and actual outcomes. By introducing this calibration step, the model receives feedback on its confidence levels and adjusts the temperature parameter accordingly, preventing overconfidence on both in-distribution and out-of-distribution data while maintaining accurate probabilistic interpretation.
Solution Approach 2:
The patent applies partial normalization through temperature scaling rather than full softmax normalization in all cases. By using a temperature parameter greater than 1, the model applies a dampening effect that reduces excessive confidence scores while still providing meaningful probabilistic interpretation. This partial action approach corrects the overconfidence problem without completely abandoning the softmax framework.
3Adaptability or versatility
If softmax is designed to model categorical distribution of single-label classification, then the output space is a probability simplex, but the categorical assumption does not hold for 0-label or N-label problems
Solution Approach 1:
The patent creates a universal normalization framework that handles single-label, 0-label, and N-label classification problems through the same L2 normalization and temperature scaling mechanism. This multi-functional approach eliminates the need for different normalization strategies for different problem types, reducing complexity while improving adaptability. The same mathematical operations serve multiple classification scenarios by adjusting the temperature parameter.
Data Source
AI summary
Embodiments include methods, and processing devices for implementing the methods. Various embodiments may include calculating a batch softmax normalization factor using a plurality of logit values from a plurality of logits of a layer of a neural network, normalizing the plurality of logit values using the batch softmax normalization factor, and mapping each of the normalized plurality of logit values to one of a plurality of manifolds in a coordinate space. In some embodiments, each of the plurality of manifolds represents a number of labels to which a logit can be classified. In some embodiments, at least one of the plurality of manifolds represents a number of labels other than one label.