Siamese Neural Networks for Comparative Feature Identification
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Solution Overview
Problem
Current machine learning techniques fail to effectively explain differences in prediction outcomes between specific instances, as they focus on individual predictions rather than comparative explanations, and often provide misleading insights due to model inadequacies.
Innovation Solution
A method using neural network architectures trained to learn similarities between input objects, which computes gradients of distance differences between encoding features to identify the contributing features of differently classified instances, and performs post-processing to reduce noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing techniques focus on individual prediction explanations, then they can identify relevant features for single instances, but they fail to identify differences between two or more specific instances
Solution Approach 1:
The patent segments the explanation task into two distinct components: (1) individual instance feature analysis and (2) comparative difference analysis. By separating these functions, the system can maintain precise feature identification for individual instances while adding specialized comparative analysis capability to identify differences between instances.
Solution Approach 2:
The patent introduces an intermediary comparative analysis layer that takes individual instance explanations as input and produces difference identification as output. This intermediary component bridges the gap between single-instance analysis and multi-instance comparison, enabling the system to leverage existing feature identification techniques while adding comparative functionality.
2Adaptability or versatility
If perturbation techniques are used to generate alternative classifications, then they may identify different class predictions, but they provide misleading explanations due to model inadequacies
Solution Approach 1:
The patent converts the harmful effect of perturbation-induced misclassifications into a beneficial feature by using the differences in model responses to perturbations as a signal for identifying important features. Instead of treating perturbation artifacts as errors to be eliminated, the system leverages them to highlight features that significantly influence classification decisions.
Solution Approach 2:
The patent implements a feedback mechanism where perturbation results are fed back into the analysis process to refine feature identification. By iteratively applying perturbations and analyzing the model's response changes, the system progressively identifies the most influential features while filtering out spurious signals caused by model inadequacies.
3Measurement precision
If gradient computation is performed on encoding features, then differences contributing to classification can be identified, but noise in gradients reduces explanation reliability
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different gradient components. Instead of uniformly processing all gradient values, the system identifies and emphasizes locally significant gradient components that correspond to important features, while suppressing or filtering out noisy local variations. This selective processing enhances the reliability of the explanation by focusing on meaningful signals.
Data Source
AI summary
A first instance of data and a second instance of data can be received, which have been classified differently. The first instance can be input to a first neural network, the first neural network generating a first encoding associated with the first instance. The second instance can be input to a second neural network the second neural network generating a second encoding associated with the second instance. The first neural network and the second neural network form neural network architecture trained to learn similarities in given pair of input objects. Based on the first encoding and the second encoding, a difference can be identified in features of the first instance and the second instance, which contributed to the first instance and the second instance being classified differently.