Autoencoder Embedding Analysis for Out-of-Sample ML Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Machine learning models, particularly in Advanced Driver Assistance Systems (ADAS), operate as 'black boxes', making it difficult for developers to understand their inner workings, especially when dealing with out-of-sample data, which conventional dimensionality reduction techniques like t-SNE and UMAP cannot effectively generalize to, leading to inefficiencies in model adaptation and data-set optimization.
Innovation Solution
Training an autoencoder to perform dimensionality reduction on data from embedding layers of machine learning models, using a custom loss function that combines reconstruction loss and error, allowing visualization and analysis of out-of-sample data, and employing a tanh activation function for enhanced understanding and model adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional dimensionality reduction techniques (t-SNE, UMAP) are used to visualize embedding layer data, then in-sample data can be effectively visualized, but out-of-sample data cannot be generalized
Solution Approach 1:
The patent trains an autoencoder model in advance using in-sample data to learn the mapping from high-dimensional embedding space to visualizable latent space. This preliminary training enables the model to generalize to out-of-sample data without requiring re-training, resolving the contradiction between adaptability and reliability.
Solution Approach 2:
The patent introduces an autoencoder as an intermediary model that bridges the gap between the ML model's embedding layer and human interpretable visualizations. The autoencoder's bottleneck layer serves as a mediator that transforms high-dimensional data into a lower-dimensional representation that preserves structural relationships while enabling generalization to unseen data.
2Device complexity
If ML models are treated as black boxes, then model complexity is reduced, but transparency and understanding of learned principles are lost
Solution Approach 1:
The patent extracts data from the embedding layer of the ML model and processes it through an autoencoder to produce visualizable representations. This extraction approach allows developers to examine the internal representations without modifying the original black box model, thereby maintaining model complexity while gaining transparency into learned principles.
3Measurement precision
If new analysis is run for each out-of-sample data assessment, then accurate model analysis is achieved, but computational cost and time consumption increase
Solution Approach 1:
The patent performs preliminary training of the autoencoder on in-sample data to establish a reusable visualization pipeline. Once trained, the autoencoder can rapidly process out-of-sample data without requiring new analysis runs, significantly reducing computational cost and time while maintaining analysis accuracy through the learned transformation mapping.
Data Source
Figure 1~2
Figure 3a~3b
Figure 4
AI summary
A method of training an autoencoder (202) for analysing a machine learning model (102), an autoencoder (202) for analysing a machine learning model (102), a system and a computer program product, the method comprising: extracting output data from an embedding layer of the machine learning model (102) operating on in-sample data, performing dimensionality reduction on at least a portion of the extracted output data from the embedding layer to obtain first dimensionality reduced data (302), and training the autoencoder (202) to generate corresponding intermediate dimensionality reduced data (304) at the bottleneck (206) of the autoencoder from the at least a portion of the extracted output data.